Skip to main content

Research Repository

Advanced Search

All Outputs (128)

Fuzzy C-means-based scenario bundling for stochastic service network design (2018)
Conference Proceeding
Jiang, X., Bai, R., Landa-Silva, D., & Aickelin, U. (2018). Fuzzy C-means-based scenario bundling for stochastic service network design. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings (1-8). https://doi.org/10.1109/SSCI.2017.8280905

Stochastic service network designs with uncertain demand represented by a set of scenarios can be modelled as a large-scale two-stage stochastic mixed-integer program (SMIP). The progressive hedging algorithm (PHA) is a decomposition method for solvi... Read More about Fuzzy C-means-based scenario bundling for stochastic service network design.

THCluster: herb supplements categorization for precision traditional Chinese medicine (2017)
Conference Proceeding
Ruan, C., Wang, Y., Zhang, Y., Ma, J., Chen, H., Aickelin, U., …Zhang, T. (2017). THCluster: herb supplements categorization for precision traditional Chinese medicine.

There has been a continuing demand for traditional and complementary medicine worldwide. A fundamental and important topic in Traditional Chinese Medicine (TCM) is to optimize the prescription and to detect herb regularities from TCM data. In this pa... Read More about THCluster: herb supplements categorization for precision traditional Chinese medicine.

Novel similarity measure for interval-valued data based on overlapping ratio (2017)
Conference Proceeding
Kabir, S., Wagner, C., Havens, T. C., Anderson, D. T., & Aickelin, U. (2017). Novel similarity measure for interval-valued data based on overlapping ratio. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-6). https://doi.org/10.1109/FUZZ-IEEE.2017.8015623

In computing the similarity of intervals, current similarity measures such as the commonly used Jaccard and Dice measures are at times not sensitive to changes in the width of intervals, producing equal similarities for substantially different pairs... Read More about Novel similarity measure for interval-valued data based on overlapping ratio.

CRNN: A Joint Neural Network for Redundancy Detection (2017)
Conference Proceeding
Fu, X., Ch’ng, E., Aickelin, U., & See, S. (2017). CRNN: A Joint Neural Network for Redundancy Detection. In 2017 IEEE International Conference on Smart Computing (SMARTCOMP) (1-8). https://doi.org/10.1109/SMARTCOMP.2017.7946996

This paper proposes a novel framework for detecting redundancy in supervised sentence categorisation. Unlike traditional singleton neural network, our model incorporates character-aware convolutional neural network (Char-CNN) with character-aware rec... Read More about CRNN: A Joint Neural Network for Redundancy Detection.

Robust datamining (2017)
Conference Proceeding
Uwe, A. (2017). Robust datamining.

Our long-term research goal is to develop datamining methodologies that are robust to changes in data and uncertainty. By robust we mean solutions remain ‘optimal’ when things change or are easily repaired. Broadly, this robustness can be achieved in... Read More about Robust datamining.

Measuring agreement on linguistic expressions in medical treatment scenarios (2016)
Conference Proceeding
Navarro, J., Wagner, C., Aickelin, U., Green, L., & Ashford, R. (2016). Measuring agreement on linguistic expressions in medical treatment scenarios. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI) (1-8). https://doi.org/10.1109/SSCI.2016.7849895

Quality of life assessment represents a key process of deciding treatment success and viability. As such, patients’ perceptions of their functional status and well-being are important inputs for impairment assessment. Given that patient completed que... Read More about Measuring agreement on linguistic expressions in medical treatment scenarios.

Exploring differences in interpretation of words essential in medical expert-patient communication (2016)
Conference Proceeding
Navarro, J., Wagner, C., Aickelin, U., Green, L., & Robert, A. (2016). Exploring differences in interpretation of words essential in medical expert-patient communication. In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

In the context of cancer treatment and surgery, quality of life assessment is a crucial part of determining treatment success and viability. In order to assess it, patient-completed questionnaires which employ words to capture aspects of patients’ we... Read More about Exploring differences in interpretation of words essential in medical expert-patient communication.

Measuring player’s behaviour change over time in public goods game (2016)
Conference Proceeding
Fattah, P., Aickelin, U., & Wagner, C. (2016). Measuring player’s behaviour change over time in public goods game.

An important issue in public goods game is whether player's behaviour changes over time, and if so, how significant it is. In this game players can be classified into different groups according to the level of their participation in the public good.... Read More about Measuring player’s behaviour change over time in public goods game.

An improved system for sentence-level novelty detection in textual streams (2016)
Conference Proceeding
Fu, X., Ch'ng, E., & Aickelin, U. (in press). An improved system for sentence-level novelty detection in textual streams. . https://doi.org/10.1049/cp.2015.0250

Novelty detection in news events has long been a difficult problem. A number of models performed well on specific data streams but certain issues are far from being solved, particularly in large data streams from the WWW where unpredictability of new... Read More about An improved system for sentence-level novelty detection in textual streams.

Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation (2015)
Conference Proceeding
Navarro, J., Wagner, C., & Aickelin, U. (2015). Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation.

Fuzzy Rule-Based Classification Systems (FRBCSs) have the potential to provide so-called interpretable classifiers, i.e. classifiers which can be introspective, understood, validated and augmented by human experts by relying on fuzzy-set based rules.... Read More about Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation.

Adaptive Data Communication Interface: A User-Centric Visual Data Interpretation Framework (2015)
Conference Proceeding
Figueredo, G. P., Wagner, C., Garibaldi, J. M., & Aickelin, U. (2015). Adaptive Data Communication Interface: A User-Centric Visual Data Interpretation Framework. . https://doi.org/10.1109/Trustcom.2015.571

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.

Identifying candidate risk factors for prescription drug side effects using causal contrast set mining (2015)
Conference Proceeding
Reps, J. M., & Aickelin, U. (2015). Identifying candidate risk factors for prescription drug side effects using causal contrast set mining.

Big longitudinal observational databases present the opportunity to extract new knowledge in a cost effective manner. Unfortunately, the ability of these databases to be used for causal inference is limited due to the passive way in which the data ar... Read More about Identifying candidate risk factors for prescription drug side effects using causal contrast set mining.

Analysing fuzzy sets through combining measures of similarity and distance (2014)
Conference Proceeding
McCulloch, J., Wagner, C., & Aickelin, U. (2014). Analysing fuzzy sets through combining measures of similarity and distance.

Reasoning with fuzzy sets can be achieved through measures such as similarity and distance. However, these measures can often give misleading results when considered independently, for example giving the same value for two different pairs of fuzzy se... Read More about Analysing fuzzy sets through combining measures of similarity and distance.

Comparison of distance metrics for hierarchical data in medical databases (2014)
Conference Proceeding
Hassan, D., Aickelin, U., & Wagner, C. (2014). Comparison of distance metrics for hierarchical data in medical databases.

Distance metrics are broadly used in different research areas and applications, such as bio-informatics, data mining and many other fields. However, there are some metrics, like pg-gram and Edit Distance used specifically for data with a hierarchical... Read More about Comparison of distance metrics for hierarchical data in medical databases.

A fuzzy directional distance measure (2014)
Conference Proceeding
McCulloch, J., Hinde, C., Wagner, C., & Aickelin, U. (2014). A fuzzy directional distance measure.

The measure of distance between two fuzzy sets is a fundamental tool within fuzzy set theory, however, distance measures currently within the literature use a crisp value to represent the distance between fuzzy sets. A real valued distance measure is... Read More about A fuzzy directional distance measure.

Tuning a multiple classifier system for side effect discovery using genetic algorithms (2014)
Conference Proceeding
Reps, J. M., Aickelin, U., & Garibaldi, J. M. (2014). Tuning a multiple classifier system for side effect discovery using genetic algorithms. In 2014 IEEE Congress on Evolutionary Computation (CEC). https://doi.org/10.1109/CEC.2014.6900328

In previous work, a novel supervised framework implementing a binary classifier was presented that obtained excellent results for side effect discovery. Interestingly, unique side effects were identified when different binary classifiers were used wi... Read More about Tuning a multiple classifier system for side effect discovery using genetic algorithms.

Variability of behaviour in electricity load profile clustering: who does things at the same time each day? (2014)
Conference Proceeding
Dent, I., Craig, T., Aickelin, U., & Rodden, T. (2014). Variability of behaviour in electricity load profile clustering: who does things at the same time each day?. In P. Perner (Ed.), Advances in data mining: applications and theoretical aspects: 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014: proceedings (70–84). https://doi.org/10.1007/978-3-319-08976-8_6

UK electricity market changes provide opportunities to alter households' electricity usage patterns for the benefit of the overall electricity network. Work on clustering similar households has concentrated on daily load profiles and the variability... Read More about Variability of behaviour in electricity load profile clustering: who does things at the same time each day?.

Refining adverse drug reactions using association rule mining for electronic healthcare data (2014)
Conference Proceeding
Reps, J. M., Aickelin, U., Ma, J., & Zhang, Y. (2014). Refining adverse drug reactions using association rule mining for electronic healthcare data.

Side effects of prescribed medications are a common occurrence. Electronic healthcare databases present the opportunity to identify new side effects efficiently but currently the methods are limited due to confounding (i.e. when an association betwee... Read More about Refining adverse drug reactions using association rule mining for electronic healthcare data.

Incorporating spontaneous reporting system data to aid causal inference in longitudinal healthcare data (2014)
Conference Proceeding
Reps, J. M., & Aickelin, U. (2014). Incorporating spontaneous reporting system data to aid causal inference in longitudinal healthcare data.

Inferring causality using longitudinal observational databases is challenging due to the passive way the data are collected. The majority of associations found within longitudinal observational data are often non-causal and occur due to confounding.... Read More about Incorporating spontaneous reporting system data to aid causal inference in longitudinal healthcare data.

Personalising mobile advertising based on users’ installed apps (2014)
Conference Proceeding
Reps, J., Aickelin, U., Garibaldi, J. M., & Damski, C. (2014). Personalising mobile advertising based on users’ installed apps.

Mobile advertising is a billion pound industry that is rapidly expanding. The success of an advert is measured based on how users interact with it. In this paper we investigate whether the application of unsupervised learning and association rule min... Read More about Personalising mobile advertising based on users’ installed apps.