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Prof. JONATHAN GARIBALDI's Outputs (67)

A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem (2024)
Journal Article
Lin, B., Li, J., Cui, T., Jin, H., Bai, R., Qu, R., & Garibaldi, J. (2024). A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem. Expert Systems with Applications, 249, Article 123515. https://doi.org/10.1016/j.eswa.2024.123515

The online bin packing problem is a well-known optimization challenge that finds application in a wide range of real-world scenarios. In the paper, we propose a novel algorithm called FuzzyPatternPack(FPP), which leverages fuzzy inference and pattern... Read More about A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem.

Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease (2022)
Journal Article
Gonem, S., Taylor, A., Figueredo, G., Forster, S., Quinlan, P., Garibaldi, J. M., McKeever, T. M., & Shaw, D. (2022). Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease. Respiratory Research, 23, Article 203. https://doi.org/10.1186/s12931-022-02130-6

Background: The National Early Warning Score-2 (NEWS-2) is used to detect patient deterioration in UK hospitals but fails to take account of the detailed granularity or temporal trends in clinical observations. We used data-driven methods to develop... Read More about Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease.

LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint (2022)
Journal Article
Jafari, M., Francis, S., Garibaldi, J. M., & Chen, X. (2022). LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint. Medical Image Analysis, 81, Article 102536. https://doi.org/10.1016/j.media.2022.102536

In medical image segmentation, supervised machine learning models trained using one image modality (e.g. computed tomography (CT)) are often prone to failure when applied to another image modality (e.g. magnetic resonance imaging (MRI)) even for the... Read More about LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint.

Lessons learned from the COVID-19 pandemic about sample access for research in the UK (2022)
Journal Article
Mai Sims, J., Lawrence, E., Glazer, K., Gander, A., Fuller, B., Garibaldi, J., …Quinlan, P. R. (2022). Lessons learned from the COVID-19 pandemic about sample access for research in the UK. BMJ Open, 12(4), Article e047309. https://doi.org/10.1136/bmjopen-2020-047309

Objective Annotated clinical samples taken from patients are a foundation of translational medical research and give mechanistic insight into drug trials. Prior research by the Tissue Directory and Coordination Centre (TDCC) indicated that researcher... Read More about Lessons learned from the COVID-19 pandemic about sample access for research in the UK.

FuzzyDCNN: Incorporating Fuzzy Integral Layers to Deep Convolutional Neural Networks for Image Segmentation (2021)
Presentation / Conference Contribution
Lin, Q., Chen, X., Chen, C., & Garibaldi, J. M. (2021). FuzzyDCNN: Incorporating Fuzzy Integral Layers to Deep Convolutional Neural Networks for Image Segmentation. In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/fuzz45933.2021.9494456

Convolutional neural networks (CNNs) have achieved the state-of-the-art performance in many application areas, due to the capability of automatically extracting and aggregating spatial and channel-wise features from images. Most recent studies have c... Read More about FuzzyDCNN: Incorporating Fuzzy Integral Layers to Deep Convolutional Neural Networks for Image Segmentation.

Designing the Hierarchical Fuzzy Systems Via FuzzyR Toolbox (2021)
Presentation / Conference Contribution
Razak, T. R., Chen, C., Garibaldi, J. M., & Wagner, C. (2021). Designing the Hierarchical Fuzzy Systems Via FuzzyR Toolbox. In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/FUZZ45933.2021.9494485

The use of Hierarchical Fuzzy Systems (HFS) has been well acknowledged as a good approach in reducing the complexity and improving the interpretability of fuzzy logic systems (FLS). Over the past years, many fuzzy logic toolkits have been made availa... Read More about Designing the Hierarchical Fuzzy Systems Via FuzzyR Toolbox.

An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems (2021)
Presentation / Conference Contribution
Chen, C., Zhao, Y., Wagner, C., Pekaslan, D., & Garibaldi, J. M. (2021). An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems. In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/fuzz45933.2021.9494472

Recent years have seen a surge in interest in non-singleton fuzzy systems. These systems enable the direct modelling of uncertainty affecting systems' inputs using the fuzzification stage. Moreover, recent work has shown how different composition app... Read More about An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems.

Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis (2020)
Journal Article
Chernbumroong, S., Johnson, J., Gupta, N., Miller, S., Mccormack, F. X., Garibaldi, J. M., & Johnson, S. R. (2021). Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis. European Respiratory Journal, 57(6), Article 2003036. https://doi.org/10.1183/13993003.03036-2020

Background: Lymphangioleiomyomatosis (LAM) is a rare multisystem disease with variable clinical manifestations and differing rates of progression that make management decisions and giving prognostic advice difficult. We used machine learning to ident... Read More about Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis.

A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems (2020)
Journal Article
D'Alterio, P., Garibaldi, J. M., John, R. I., & Wagner, C. (2021). A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 29(11), 3323-3333. https://doi.org/10.1109/TFUZZ.2020.3018379

Constrained interval type-2 (CIT2) fuzzy sets have been introduced to preserve interpretability when moving from type-1 to interval type-2 (IT2) membership functions. Although they can be used to produce type-2 fuzzy systems with enhanced explainabil... Read More about A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems.

End-to-End Fovea Localisation in Colour Fundus Images with a Hierarchical Deep Regression Network (2020)
Journal Article
Xie, R., Liu, J., Cao, R., Qiu, C. S., Duan, J., Garibaldi, J., & Qiu, G. (2020). End-to-End Fovea Localisation in Colour Fundus Images with a Hierarchical Deep Regression Network. IEEE Transactions on Medical Imaging, 40(1), 116-128. https://doi.org/10.1109/TMI.2020.3023254

Accurately locating the fovea is a prerequisite for developing computer aided diagnosis (CAD) of retinal diseases. In colour fundus images of the retina, the fovea is a fuzzy region lacking prominent visual features and this makes it difficult to dir... Read More about End-to-End Fovea Localisation in Colour Fundus Images with a Hierarchical Deep Regression Network.

FuzzyR: An Extended Fuzzy Logic Toolbox for the R Programming Language (2020)
Presentation / Conference Contribution
Chen, C., Razak, T. R., & Garibaldi, J. M. (2020). FuzzyR: An Extended Fuzzy Logic Toolbox for the R Programming Language. In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-8). https://doi.org/10.1109/fuzz48607.2020.9177780

This paper presents an R package FuzzyR which is an extended fuzzy logic toolbox for the R programming language. FuzzyR is a continuation of the previous Fuzzy R toolboxes such as FuzzyToolkitUoN. Whilst keeping existing functionalities of the previo... Read More about FuzzyR: An Extended Fuzzy Logic Toolbox for the R Programming Language.

Constrained Interval Type-2 Fuzzy Classification Systems for Explainable AI (XAI) (2020)
Presentation / Conference Contribution
D'Alterio, P., Garibaldi, J. M., & John, R. I. (2020). Constrained Interval Type-2 Fuzzy Classification Systems for Explainable AI (XAI). In Proceedings of IEEE World Congress on Computational Intelligence (WCCI) 2020

In recent year, there has been a growing need for intelligent systems that not only are able to provide reliable classifications but can also produce explanations for the decisions they make. The demand for increased explainability has led to the eme... Read More about Constrained Interval Type-2 Fuzzy Classification Systems for Explainable AI (XAI).

Juzzy Constrained: Software for Constrained Interval Type-2 Fuzzy Sets and Systems in Java (2020)
Presentation / Conference Contribution
D'Alterio, P., Garibaldi, J. M., John, R. I., & Wagner, C. (2020). Juzzy Constrained: Software for Constrained Interval Type-2 Fuzzy Sets and Systems in Java. In Proceedings of IEEE World Congress on Computational Intelligence (WCCI) 2020

Constrained interval type-2 (CIT2) fuzzy sets are a class of type-2 fuzzy sets that has been recently proposed as a way to extend type-1 membership functions to interval type-2 (IT2) while keeping a semantic connection between the IT2 fuzzy set and t... Read More about Juzzy Constrained: Software for Constrained Interval Type-2 Fuzzy Sets and Systems in Java.

Performance and Interpretability in Fuzzy Logic Systems – can we have both? (2020)
Presentation / Conference Contribution
Pekaslan, D., Chen, C., Wagner, C., & Garibaldi, J. M. (2020). Performance and Interpretability in Fuzzy Logic Systems – can we have both?.

Fuzzy Logic Systems can provide a good level of interpretability and may provide a key building block as part of a growing interest in explainable AI. In practice, the level of interpretability of a given fuzzy logic system is dependent on how well i... Read More about Performance and Interpretability in Fuzzy Logic Systems – can we have both?.

A Comprehensive Study of the Efficiency of Type-Reduction Algorithms (2020)
Journal Article
Chen, C., Wu, D., Garibaldi, J. M., John, R. I., Twycross, J., & Mendel, J. M. (2021). A Comprehensive Study of the Efficiency of Type-Reduction Algorithms. IEEE Transactions on Fuzzy Systems, 29(6), 1556 -1566. https://doi.org/10.1109/tfuzz.2020.2981002

Improving the efficiency of type-reduction algorithms continues to attract research interest. Recently, there have been some new type-reduction approaches claiming that they are more efficient than the well-known algorithms such as the enhanced Karni... Read More about A Comprehensive Study of the Efficiency of Type-Reduction Algorithms.

Uncertainty-Aware Forecasting of Renewable Energy Sources (2020)
Presentation / Conference Contribution
Pekaslan, D., Wagner, C., Garibaldi, J. M., Marín, L. G., & Sáez, D. (2020). Uncertainty-Aware Forecasting of Renewable Energy Sources. In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). https://doi.org/10.1109/bigcomp48618.2020.00-68

Smart grid systems are designed to enable the efficient capture and intelligent distribution of electricity across a distributed set of utilities. They are an essential component of increasingly important renewable energy sources, where it is vital t... Read More about Uncertainty-Aware Forecasting of Renewable Energy Sources.

Constrained Interval Type-2 Fuzzy Sets (2020)
Journal Article
Dalterio, P., Garibaldi, J. M., John, R., & Pourabdollah, A. (2021). Constrained Interval Type-2 Fuzzy Sets. IEEE Transactions on Fuzzy Systems, 29(5), 1212-1225. https://doi.org/10.1109/tfuzz.2020.2970911

In many contexts, type-2 fuzzy sets are obtained from a type-1 fuzzy set to which we wish to add uncertainty. However, in the current type-2 representation there is no restriction on the shape of the footprint of uncertainty and the embedded sets tha... Read More about Constrained Interval Type-2 Fuzzy Sets.

Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach (2020)
Journal Article
Soria, D., Razak, T. R., Garibaldi, J. M., Pourabdollah, A., & Wagner, C. (2021). Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach. IEEE Transactions on Fuzzy Systems, 29(5), 1160-1172. https://doi.org/10.1109/tfuzz.2020.2969901

Hierarchical fuzzy systems (HFSs) have been shown to have the potential to improve the interpretability of fuzzy logic systems (FLSs). However, challenges remain, such as: "How can we measure their interpretability?", "How can we make an informed ass... Read More about Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach.

FU-Net: Multi-class Image Segmentation Using Feedback Weighted U-Net (2019)
Book Chapter
Jafari, M., Li, R., Xing, Y., Auer, D., Francis, S., Garibaldi, J., & Chen, X. (2019). FU-Net: Multi-class Image Segmentation Using Feedback Weighted U-Net. In Image and Graphics: 10th International Conference, ICIG 2019, Beijing, China, August 23–25, 2019, Proceedings, Part II (529-537). Springer Verlag. https://doi.org/10.1007/978-3-030-34110-7_44

© 2019, Springer Nature Switzerland AG. In this paper, we present a generic deep convolutional neural network (DCNN) for multi-class image segmentation. It is based on a well-established supervised end-to-end DCNN model, known as U-net. U-net is firs... Read More about FU-Net: Multi-class Image Segmentation Using Feedback Weighted U-Net.

A Measure of Structural Complexity of Hierarchical Fuzzy Systems Adapted from Software Engineering (2019)
Presentation / Conference Contribution
Razak, T. R., Garibaldi, J. M., & Wagner, C. (2019). A Measure of Structural Complexity of Hierarchical Fuzzy Systems Adapted from Software Engineering. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) ( 1-7). https://doi.org/10.1109/FUZZ-IEEE.2019.8859011

Hierarchical fuzzy systems (HFSs) have been seen as an effective approach to reduce the complexity of fuzzy logic systems (FLSs), largely as a result of reducing the number of rules. However, it is not clear completely how complexity of HFSs can be m... Read More about A Measure of Structural Complexity of Hierarchical Fuzzy Systems Adapted from Software Engineering.

Deep Fuzzy Tree for Large-Scale Hierarchical Visual Classification (2019)
Journal Article
Wang, Y., Hu, Q., Zhu, P., Li, L., Lu, B., Garibaldi, J. M., & Li, X. (2020). Deep Fuzzy Tree for Large-Scale Hierarchical Visual Classification. IEEE Transactions on Fuzzy Systems, 28(7), 1395-1406. https://doi.org/10.1109/tfuzz.2019.2936801

Deep learning models often use a flat softmax layer to classify samples after feature extraction in visual classification tasks. However, it is hard to make a single decision of finding the true label from massive classes. In this scenario, hierarchi... Read More about Deep Fuzzy Tree for Large-Scale Hierarchical Visual Classification.

ADONiS - Adaptive Online Non-Singleton Fuzzy Logic Systems (2019)
Journal Article
Pekaslan, D., Wagner, C., & Garibaldi, J. M. (2020). ADONiS - Adaptive Online Non-Singleton Fuzzy Logic Systems. IEEE Transactions on Fuzzy Systems, 28(10), 2302-2312. https://doi.org/10.1109/tfuzz.2019.2933787

Non-Singleton Fuzzy Logic Systems (NSFLSs) have the potential to capture and handle input noise within the design of input fuzzy sets. In this paper, we propose an online learning method which utilises a sequence of observations to continuously updat... Read More about ADONiS - Adaptive Online Non-Singleton Fuzzy Logic Systems.

A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets (2019)
Presentation / Conference Contribution
Shen, Z., Chen, X., & Garibaldi, J. M. (2019). A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-6). https://doi.org/10.1109/FUZZ-IEEE.2019.8858890

In this paper, we propose a novel weighted combination feature selection method using bootstrap and fuzzy sets. The proposed method mainly consists of three processes, including fuzzy sets generation using bootstrap, weighted combination of fuzzy set... Read More about A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets.

Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels (2019)
Presentation / Conference Contribution
Pekaslan, D., Wagner, C., & Garibaldi, J. M. (2019). Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-7). https://doi.org/10.1109/FUZZ-IEEE.2019.8858800

Most real-world environments are subject to different sources of uncertainty which may vary in magnitude over time. We propose that while Type-1 (T1) Non-Singleton Fuzzy Logic System (NSFLSs) have the potential to tackle uncertainty within the input... Read More about Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels.

On the Concept of Meaningfulness in Constrained Type-2 Fuzzy Sets (2019)
Presentation / Conference Contribution
D'Alterio, P., Garibaldi, J. M., & John, R. (2019). On the Concept of Meaningfulness in Constrained Type-2 Fuzzy Sets. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/FUZZ-IEEE.2019.8858942

Constrained type-2 fuzzy sets have been proposed as a tool to model type-2 fuzzy sets starting from a type-1 generator set with uncertainty. This constrained representation only defines as acceptable the embedded sets that have the same shape as the... Read More about On the Concept of Meaningfulness in Constrained Type-2 Fuzzy Sets.

Interpretability and Complexity of Design in the Creation of Fuzzy Logic Systems — A User Study (2018)
Presentation / Conference Contribution
Rosli Razak, T. R., Garibaldi, J. M., Wagner, C., Pourabdollah, A., & Soria, D. (2018, November). Interpretability and Complexity of Design in the Creation of Fuzzy Logic Systems — A User Study. Presented at 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India

In recent years, researchers have become increasingly more interested in designing an interpretable Fuzzy Logic System (FLS). Many studies have claimed that reducing the complexity of FLSs can lead to improved model interpretability. That is, reducin... Read More about Interpretability and Complexity of Design in the Creation of Fuzzy Logic Systems — A User Study.

A classification-regression deep learning model for people counting (2018)
Presentation / Conference Contribution
Xu, B., Zou, W., Garibaldi, J., & Qiu, G. (2018). A classification-regression deep learning model for people counting. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Intelligent Systems and Applications Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1 (136-149). https://doi.org/10.1007/978-3-030-01054-6_9

In this paper, we construct a multi-task deep learning model to simultaneously predict people number and the level of crowd density. Motivated by the success of applying " ambiguous labelling " to age estimation problem, we also manage to employ this... Read More about A classification-regression deep learning model for people counting.

Noise Parameter Estimation for Non-Singleton Fuzzy Logic Systems (2018)
Presentation / Conference Contribution
Pekaslan, D., Garibaldi, J. M., & Wagner, C. (2018). Noise Parameter Estimation for Non-Singleton Fuzzy Logic Systems. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) ( 2960-2965). https://doi.org/10.1109/SMC.2018.00503

Real-world environments face a wide range of noise (uncertainty) sources and gaining insight into the level of noise is a critical part of many applications. While Non-Singleton Fuzzy Logic Systems (NSFLSs), in particular recently introduced advanced... Read More about Noise Parameter Estimation for Non-Singleton Fuzzy Logic Systems.

An end-to-end deep learning histochemical scoring system for breast cancer TMA (2018)
Journal Article
Liu, J., Xu, B., Zheng, C., Gong, Y., Garibaldi, J., Soria, D., …Qiu, G. (2019). An end-to-end deep learning histochemical scoring system for breast cancer TMA. IEEE Transactions on Medical Imaging, 38(2), 617-628. https://doi.org/10.1109/TMI.2018.2868333

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain tumor tissues prepared on tissue microarray (TMA). To determine the molecul... Read More about An end-to-end deep learning histochemical scoring system for breast cancer TMA.

A comment on "A direct approach for determining the switch points in the Karnik-Mendel algorithm" (2018)
Journal Article
Chen, C., Wu, D., Garibaldi, J. M., John, R., Twycross, J., & Mendel, J. M. (2018). A comment on "A direct approach for determining the switch points in the Karnik-Mendel algorithm". IEEE Transactions on Fuzzy Systems, 26(6), 3905-3907. https://doi.org/10.1109/tfuzz.2018.2865134

This letter is a supplement to the previous paper “A Direct Approach for Determining the Switch Points in the Karnik-Mendel Algorithm”. In the previous paper, the enhanced iterative algorithm with stop condition (EIASC) was shown to be the most ineff... Read More about A comment on "A direct approach for determining the switch points in the Karnik-Mendel algorithm".

Exploring subsethood to determine firing strength in non-singleton fuzzy logic systems (2018)
Presentation / Conference Contribution
Pekaslan, D., Garibaldi, J. M., & Wagner, C. (2018). Exploring subsethood to determine firing strength in non-singleton fuzzy logic systems.

Real world environments face a wide range of sources of noise and uncertainty. Thus, the ability to handle various uncertainties, including noise, becomes an indispensable element of automated decision making. Non-Singleton Fuzzy Logic Systems (NSFLS... Read More about Exploring subsethood to determine firing strength in non-singleton fuzzy logic systems.

Exploring Constrained Type-2 fuzzy sets (2018)
Presentation / Conference Contribution
D’Alterio, P., Garibaldi, J. M., & Pourabdollah, A. (2018). Exploring Constrained Type-2 fuzzy sets.

Fuzzy logic has been widely used to model human reasoning thanks to its inherent capability of handling uncertainty. In particular, the introduction of Type-2 fuzzy sets added the possibility of expressing uncertainty even on the definition of the me... Read More about Exploring Constrained Type-2 fuzzy sets.

Direct Application of Convolutional Neural Network Features to Image Quality Assessment (2018)
Presentation / Conference Contribution
Hou, X., Sun, K., Liu, B., Gong, Y., Garibaldi, J., & Qiu, G. (2018). Direct Application of Convolutional Neural Network Features to Image Quality Assessment. In 2018 IEEE Visual Communications and Image Processing (VCIP). https://doi.org/10.1109/VCIP.2018.8698726

© 2018 IEEE. We take advantage of the popularity of deep con-volutional neural networks (CNNs) and have developed a very simple image quality assessment method that rivals state of the art. We show that convolutional layer outputs (deep features) of... Read More about Direct Application of Convolutional Neural Network Features to Image Quality Assessment.

Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms (2018)
Presentation / Conference Contribution
Agrawal, U., Pinar, A. J., Wagner, C., Havens, T. C., Soria, D., & Garibaldi, J. M. (2018). Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms.

The Fuzzy Integral (FI) is a non-linear aggregation operator which enables the fusion of information from multiple sources in respect to a Fuzzy Measure (FM) which captures the worth of both the individual sources and all their possible combinations.... Read More about Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms.

A fast community detection method in bipartite networks by distance dynamics (2017)
Journal Article
Sun, H.-L., Ch'ng, E., Yong, X., Garibaldi, J. M., See, S., & Chen, D.-B. (2018). A fast community detection method in bipartite networks by distance dynamics. Physica A: Statistical Mechanics and its Applications, 496, https://doi.org/10.1016/j.physa.2017.12.099

Many real bipartite networks are found to be divided into two-mode communities. In this paper, we formulate a new two-mode community detection algorithm BiAttractor. It is based on distance dynamics model Attractor proposed by Shao et al. with extens... Read More about A fast community detection method in bipartite networks by distance dynamics.

Determining firing strengths through a novel similarity measure to enhance uncertainty handling in non-singleton fuzzy logic systems (2017)
Presentation / Conference Contribution
Pekaslan, D., Kabir, S., Wagner, C., & Garibaldi, J. M. (2017). Determining firing strengths through a novel similarity measure to enhance uncertainty handling in non-singleton fuzzy logic systems. In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 0IJCCI (83-90). https://doi.org/10.5220/0006502000830090

Non-singleton Fuzzy Logic Systems have the potential to tackle uncertainty within the design of fuzzy systems. The inference process has a major role in determining results, being partly based on the interaction of input and antecedent fuzzy sets (in... Read More about Determining firing strengths through a novel similarity measure to enhance uncertainty handling in non-singleton fuzzy logic systems.

Vehicle incident hot spots identification: An approach for big data (2017)
Presentation / Conference Contribution
Triguero, I., Figueredo, G. P., Mesgarpour, M., Garibaldi, J. M., & John, R. (2017). Vehicle incident hot spots identification: An approach for big data. In Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications; 11th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE); and 14th IEEE International Conference on Embedded Software and Systems, (901-908). https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.329

In this work we introduce a fast big data approach for road incident hot spot identification using Apache Spark. We implement an existing immuno-inspired mechanism, namely SeleSup, as a series of MapReduce-like operations. SeleSup is composed of a nu... Read More about Vehicle incident hot spots identification: An approach for big data.

An improved game-theoretic approach to uncover overlapping communities (2017)
Journal Article
Sun, H.-L., Ch'ng, E., Yong, X., Garibaldi, J. M., See, S., & Chen, D.-B. (2017). An improved game-theoretic approach to uncover overlapping communities. International Journal of Modern Physics C, 28(8), Article 1750112. https://doi.org/10.1142/S0129183117501121

How can we uncover overlapping communities from complex networks to understand the inherent structures and functions? Chen et al. firstly proposed a community game (Game) to study this problem, and the overlapping communities have been discovered whe... Read More about An improved game-theoretic approach to uncover overlapping communities.

An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots (2017)
Journal Article
Figueredo, G. P., Triguero, I., Mesgarpour, M., Maciel Guerra, A., Garibaldi, J. M., & John, R. (2017). An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots. IEEE Transactions on Emerging Topics in Computational Intelligence, 1(4), 248-258. https://doi.org/10.1109/TETCI.2017.2721960

We report on the adaptation of an immune-inspired instance selection technique to solve a real-world big data problem of determining vehicle incident hot spots. The technique, which is inspired by the Immune System self-regulation mechanism, was orig... Read More about An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots.

Exploring the use of type-2 fuzzy sets in multi-criteria decision making based on TOPSIS (2017)
Presentation / Conference Contribution
Madi, E., Garibaldi, J. M., & Wagner, C. (2017). Exploring the use of type-2 fuzzy sets in multi-criteria decision making based on TOPSIS. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-6). https://doi.org/10.1109/FUZZ-IEEE.2017.8015664

Multi-criteria decision making (MCDM) problems are a well known category of decision making problem that has received much attention in the literature, with a key approach being the Technique for Order Preference by Similarity to Ideal Solution (TOPS... Read More about Exploring the use of type-2 fuzzy sets in multi-criteria decision making based on TOPSIS.

A new accuracy measure based on bounded relative error for time series forecasting (2017)
Journal Article
Chen, C., Twycross, J., & Garibaldi, J. M. (2017). A new accuracy measure based on bounded relative error for time series forecasting. PLoS ONE, 12(3), Article e0174202. https://doi.org/10.1371/journal.pone.0174202

Many accuracy measures have been proposed in the past for time series forecasting comparisons. However, many of these measures suffer from one or more issues such as poor resistance to outliers and scale dependence. In this paper, while summarising c... Read More about A new accuracy measure based on bounded relative error for time series forecasting.

Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets (2017)
Journal Article
Zhang, J.-H., Xia, J.-J., Garibaldi, J. M., Groumpos, P. P., & Wang, R.-B. (2017). Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets. Computer Methods and Programs in Biomedicine, 144, https://doi.org/10.1016/j.cmpb.2017.03.016

Background and objective: In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must b... Read More about Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets.

Validation of a quantifier-based fuzzy classification system for breast cancer patients on external independent cohorts (2016)
Presentation / Conference Contribution
Soria, D., & Garibaldi, J. M. (2016). Validation of a quantifier-based fuzzy classification system for breast cancer patients on external independent cohorts.

Recent studies in breast cancer domains have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a variety of unsupervised learning techniques. Consensus among the clustering algorithms has been used to categ... Read More about Validation of a quantifier-based fuzzy classification system for breast cancer patients on external independent cohorts.

On Using Genetic Algorithm for Initialising Semi-supervised Fuzzy c-Means Clustering (2016)
Journal Article
Lai, D. T. C., & Garibaldi, J. M. (2017). On Using Genetic Algorithm for Initialising Semi-supervised Fuzzy c-Means Clustering. Advances in Intelligent Systems and Computing, 532, 3-14. https://doi.org/10.1007/978-3-319-48517-1_1

In a previous work, suitable initialisation techniques were incorporated with semi-supervised Fuzzy c-Means clustering (ssFCM) to improve clustering results on a trial and error basis. In this work, we present a single fully-automatic version of an e... Read More about On Using Genetic Algorithm for Initialising Semi-supervised Fuzzy c-Means Clustering.

An exploration of issues and limitations in current methods of TOPSIS and fuzzy TOPSIS (2016)
Presentation / Conference Contribution
Madi, E., Garibaldi, J. M., & Wagner, C. (2016). An exploration of issues and limitations in current methods of TOPSIS and fuzzy TOPSIS.

Decision making is an important process for organizations. Common practice involves evaluation of prioritized alternatives based on a given set of criteria. These criteria conflict with each other and commonly no solution can satisfy all criteria sim... Read More about An exploration of issues and limitations in current methods of TOPSIS and fuzzy TOPSIS.

A multi-cycled sequential memetic computing approach for constrained optimisation (2016)
Journal Article
Sun, J., Garibaldi, J. M., Zhang, Y., & Al-Shawabkeh, A. (2016). A multi-cycled sequential memetic computing approach for constrained optimisation. Information Sciences, 340-341, 175-190. https://doi.org/10.1016/j.ins.2016.01.003

In this paper, we propose a multi-cycled sequential memetic computing structure for constrained optimisation. The structure is composed of multiple evolutionary cycles. At each cycle, an evolutionary algorithm is considered as an operator, and connec... Read More about A multi-cycled sequential memetic computing approach for constrained optimisation.

Adaptive Data Communication Interface: A User-Centric Visual Data Interpretation Framework (2015)
Presentation / Conference Contribution
Figueredo, G. P., Wagner, C., Garibaldi, J. M., & Aickelin, U. (2015, August). Adaptive Data Communication Interface: A User-Centric Visual Data Interpretation Framework. Presented at Proceedings - 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015, Helsinki, Finland

In this position paper, we present ideas about creating a next generation framework towards an adaptive interface for data communication and visualisation systems. Our objective is to develop a system that accepts large data sets as inputs and provid... Read More about Adaptive Data Communication Interface: A User-Centric Visual Data Interpretation Framework.

A comparison between two types of Fuzzy TOPSIS method (2015)
Presentation / Conference Contribution
Madi, E., Garibaldi, J. M., & Wagner, C. (2015). A comparison between two types of Fuzzy TOPSIS method.

Multi Criteria Decision Making methods have been developed to solve complex real-world decision problems. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is currently one of the most popular methods and has been shown to p... Read More about A comparison between two types of Fuzzy TOPSIS method.

A supervised adverse drug reaction signalling framework imitating Bradford Hill’s causality considerations (2015)
Journal Article
Reps, J. M., Garibaldi, J. M., Aickelin, U., Gibson, J. E., & Hubbard, R. B. (2015). A supervised adverse drug reaction signalling framework imitating Bradford Hill’s causality considerations. Journal of Biomedical Informatics, 56, https://doi.org/10.1016/j.jbi.2015.06.011

Big longitudinal observational medical data potentially hold a wealth of information and have been recognised as potential sources for gaining new drug safety knowledge. Unfortunately there are many complexities and underlying issues when analysing l... Read More about A supervised adverse drug reaction signalling framework imitating Bradford Hill’s causality considerations.

Augmented Neural Networks for modelling consumer indebtness (2014)
Presentation / Conference Contribution
Ladas, A., M. Garibaldi, J., Scarpel, R., & Aickelin, U. (2014). Augmented Neural Networks for modelling consumer indebtness. Proceedings of International Joint Conference on Neural Networks, 3086-3093. https://doi.org/10.1109/IJCNN.2014.6889760

Consumer Debt has risen to be an important problem of modern societies, generating a lot of research in order to understand the nature of consumer indebtness, which so far its modelling has been carried out by statistical models. In this work we show... Read More about Augmented Neural Networks for modelling consumer indebtness.

From Interval-Valued Data to General Type-2 Fuzzy Sets (2014)
Journal Article
Wagner, C., Miller, S., Garibaldi, J. M., Anderson, D. T., & Havens, T. C. (2015). From Interval-Valued Data to General Type-2 Fuzzy Sets. IEEE Transactions on Fuzzy Systems, 23(2), 248-269. https://doi.org/10.1109/tfuzz.2014.2310734

In this paper, a new approach is presented to model interval-based data using fuzzy sets (FSs). Specifically, we show how both crisp and uncertain intervals (where there is uncertainty about the endpoints of intervals) collected from individual or mu... Read More about From Interval-Valued Data to General Type-2 Fuzzy Sets.

Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs (2014)
Journal Article
Reps, J., M. Garibaldi, J., Aickelin, U., Soria, D., E. Gibson, J., & B. Hubbard, R. (2014). Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs. Drug Safety, 37(3), 163-170. https://doi.org/10.1007/s40264-014-0137-z

Background: Children are frequently prescribed medication `o-label', meaning there has not been sucient testing of the medication to determine its safety or eectiveness. The main reason this safety knowledge is lacking is due to
ethical restrictions... Read More about Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs.

A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery (2013)
Journal Article
Reps, J. M., Garibaldi, J. M., Aickelin, U., Soria, D., Gibson, J. E., & Hubbard, R. B. (2014). A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery. IEEE Journal of Biomedical and Health Informatics, 18(2), 537-547. https://doi.org/10.1109/JBHI.2013.2281505

Drugs are frequently prescribed to patients with the aim of improving each patient's medical state, but an unfortunate consequence of most prescription drugs is the occurrence of undesirable side effects. Side effects that occur in more than one in a... Read More about A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery.

A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised Fuzzy c-means (2013)
Journal Article
Lai, D. T. C., Garibaldi, J. M., Soria, D., & Roadknight, C. M. (2014). A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised Fuzzy c-means. Central European Journal of Operations Research, 22(3), 475-499. https://doi.org/10.1007/s10100-013-0318-3

Previously, a semi-manual method was used to identify six novel and clinically useful classes in the Nottingham Tenovus Breast Cancer dataset. 663 out of 1,076 patients were classified. The objectives of our work is three folds. Firstly, our primary... Read More about A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised Fuzzy c-means.

A quantifier-based fuzzy classification system for breast cancer patients (2013)
Journal Article
Soria, D., Garibaldi, J. M., Green, A. R., Powe, D. G., Nolan, C. C., Lemetre, C., …Ellis, I. O. (2013). A quantifier-based fuzzy classification system for breast cancer patients. Artificial Intelligence in Medicine, 58(3), https://doi.org/10.1016/j.artmed.2013.04.006

Objectives:Recent studies of breast cancer data have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a range of different clustering techniques. Consensus between unsupervised classification algorithms ha... Read More about A quantifier-based fuzzy classification system for breast cancer patients.

Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data (2012)
Journal Article
Glaab, E., Bacardit, J., Garibaldi, J. M., & Krasnogor, N. (2012). Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data. PLoS ONE, 7(7), Article e39932. https://doi.org/10.1371/journal.pone.0039932

Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new... Read More about Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data.

Discovering sequential patterns in a UK general practice database (2012)
Presentation / Conference Contribution
Reps, J., Garibaldi, J. M., Aickelin, U., Soria, D., E. Gibson, J. E., & Hubbard, R. B. (2012). Discovering sequential patterns in a UK general practice database.

The wealth of computerised medical information becoming readily available presents the opportunity to examine patterns of illnesses, therapies and responses. These patterns may be able to predict illnesses that a patient is likely to develop, allowin... Read More about Discovering sequential patterns in a UK general practice database.

MysiRNA-designer: a workflow for efficient siRNA design (2011)
Journal Article
Mysara, M., Garibaldi, J. M., & ElHefnawi, M. (2011). MysiRNA-designer: a workflow for efficient siRNA design. PLoS ONE, 6(10), Article e25642. https://doi.org/10.1371/journal.pone.0025642

The design of small interfering RNA (siRNA) is a multi factorial problem that has gained the attention of many researchers in the area of therapeutic and functional genomics. MysiRNA score was previously introduced that improves the correlation of si... Read More about MysiRNA-designer: a workflow for efficient siRNA design.

A "non-parametric" version of the naive Bayes classifier (2011)
Journal Article
Soria, D., Garibaldi, J. M., Ambrogi, F., Biganzoli, E. M., & Ellis, I. O. (2011). A "non-parametric" version of the naive Bayes classifier. Knowledge-Based Systems, 24(6), https://doi.org/10.1016/j.knosys.2011.02.014

Many algorithms have been proposed for the machine learning task of classication. One of the simplest methods, the naive Bayes classifyer, has often been found to give good performance despite the fact that its underlying assumptions (of independence... Read More about A "non-parametric" version of the naive Bayes classifier.

Learning pathway-based decision rules to classify microarray cancer samples (2010)
Presentation / Conference Contribution
Glaab, E., Garibaldi, J. M., & Krasnogor, N. (2010). Learning pathway-based decision rules to classify microarray cancer samples.

Despite recent advances in DNA chip technology current microarray gene expression studies are still affected by high noise levels, small sample sizes and large numbers of uninformative genes. Combining microarray data with cellular pathway data by us... Read More about Learning pathway-based decision rules to classify microarray cancer samples.

A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients (2010)
Journal Article
Soria, D., Garibaldi, J. M., Ambrogi, F., Green, A. R., Powe, D., Rakha, E., …Ellis, I. O. (2010). A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients. Computers in Biology and Medicine, 40(3), https://doi.org/10.1016/j.compbiomed.2010.01.003

Single clustering methods have often been used to elucidate clusters in high dimensional medical data, even though reliance on a single algorithm is known to be problematic. In this paper, we present a methodology to determine a set of ‘core classes’... Read More about A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients.

A novel framework to elucidate core classes in a dataset (2010)
Presentation / Conference Contribution
Soria, D., & Garibaldi, J. M. (2010). A novel framework to elucidate core classes in a dataset.

In this paper we present an original framework to extract representative groups from a dataset, and we validate it
over a novel case study. The framework specifies the application of different clustering algorithms, then several statistical and visu... Read More about A novel framework to elucidate core classes in a dataset.

Cancer profiles by Affinity Propagation (2009)
Journal Article
Soria, D., Garibaldi, J. M., Ambrogi, F., Boracchi, P., Raimondi, E., & Biganzoli, E. M. (2009). Cancer profiles by Affinity Propagation. International Journal of Knowledge Engineering and Soft Data Paradigms, 1(3), https://doi.org/10.1504/IJKESDP.2009.028814

The Affinity Propagation algorithm is applied to various problems of breast and cutaneous tumours subtyping using traditional biologic markers. The algorithm provides a procedure to determine the number of profiles to be considered. Well know breast... Read More about Cancer profiles by Affinity Propagation.

A comparison of three different methods for classification of breast cancer data (2008)
Presentation / Conference Contribution
Soria, D., Garibaldi, J. M., Biganzoli, E. M., & Ellis, I. O. (2008). A comparison of three different methods for classification of breast cancer data.

The classification of breast cancer patients is of great importance in cancer diagnosis. During the last few years, many algorithms have been proposed for this task. In this paper, we review different supervised machine learning techniques for classi... Read More about A comparison of three different methods for classification of breast cancer data.

Clustering breast cancer data by consensus of different validity indices
Presentation / Conference Contribution
Soria, D., Garibaldi, J. M., Ambrogi, F., Lisboa, P. J., Boracchi, P., & Biganzoli, E. M. Clustering breast cancer data by consensus of different validity indices.

Clustering algorithms will, in general, either partition a given data set into a pre-specified number of clusters or will produce a hierarchy of clusters. In this paper we analyse several different clustering techniques and apply them to a particular... Read More about Clustering breast cancer data by consensus of different validity indices.