All Outputs (34)

Local-global methods for generalised solar irradiance forecasting (2024)
Journal Article
Cargan, T. R., Landa-Silva, D., & Triguero, I. (2024). Local-global methods for generalised solar irradiance forecasting. Applied Intelligence, 54(2), 2225-2247. https://doi.org/10.1007/s10489-024-05273-9

For efficient operation, solar power operators often require generation forecasts for multiple sites with varying data availability. Many proposed methods for forecasting solar irradiance / solar power production formulate the problem as a time-serie... Read More about Local-global methods for generalised solar irradiance forecasting.

General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance (2023)
Journal Article
Triguero, I., Molina, D., Poyatos, J., Del Ser, J., & Herrera, F. (2024). General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance. Information Fusion, 103, Article 102135. https://doi.org/10.1016/j.inffus.2023.102135

Most applications of Artificial Intelligence (AI) are designed for a confined and specific task. However, there are many scenarios that call for a more general AI, capable of solving a wide array of tasks without being specifically designed for them.... Read More about General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance.

Explaining time series classifiers through meaningful perturbation and optimisation (2023)
Journal Article
Meng, H., Wagner, C., & Triguero, I. (2023). Explaining time series classifiers through meaningful perturbation and optimisation. Information Sciences, 645, Article 119334. https://doi.org/10.1016/j.ins.2023.119334

Machine learning approaches have enabled increasingly powerful time series classifiers. While performance has improved drastically, the resulting classifiers generally suffer from poor explainability, limiting their applicability in critical areas. S... Read More about Explaining time series classifiers through meaningful perturbation and optimisation.

Identifying bird species by their calls in Soundscapes (2023)
Journal Article
Maclean, K., & Triguero, I. (2023). Identifying bird species by their calls in Soundscapes. Applied Intelligence, 53, 21485-21499. https://doi.org/10.1007/s10489-023-04486-8

In many real data science problems, it is common to encounter a domain mismatch between the training and testing datasets, which means that solutions designed for one may not transfer well to the other due to their differences. An example of such was... Read More about Identifying bird species by their calls in Soundscapes.

Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort (2022)
Journal Article
Fainberg, H. P., Oldham, J. M., Molyneau, P. L., Allen, R. J., Kraven, L. M., Fahy, W. A., …Jenkins, R. G. (2022). Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort. The Lancet. Digital Health, 4(12), e862-e872. https://doi.org/10.1016/S2589-7500%2822%2900173-X

Background: Idiopathic Pulmonary Fibrosis (IPF) is a progressive fibrotic lung disease with a variable clinical trajectory. Decline in Forced Vital Capacity (FVC) is the main indicator of progression, however missingness prevents long-term analysis o... Read More about Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort.

A fusion spatial attention approach for few-shot learning (2021)
Journal Article
Song, H., Deng, B., Pound, M., Özcan, E., & Triguero, I. (2022). A fusion spatial attention approach for few-shot learning. Information Fusion, 81, 187-202. https://doi.org/10.1016/j.inffus.2021.11.019

Few-shot learning is a challenging problem in computer vision that aims to learn a new visual concept from very limited data. A core issue is that there is a large amount of uncertainty introduced by the small training set. For example, the few image... Read More about A fusion spatial attention approach for few-shot learning.

Beyond global and local multi-target learning (2021)
Journal Article
Basgalupp, M., Cerri, R., Schietgat, L., Triguero, I., & Vens, C. (2021). Beyond global and local multi-target learning. Information Sciences, 579, 508-524. https://doi.org/10.1016/j.ins.2021.08.022

In multi-target prediction, an instance has to be classified along multiple target variables at the same time, where each target represents a category or numerical value. There are several strategies to tackle multi-target prediction problems: the lo... Read More about Beyond global and local multi-target learning.

EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification (2020)
Journal Article
Le, H. L., Landa-Silva, D., Galar, M., Garcia, S., & Triguero, I. (2021). EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification. Applied Soft Computing, 101, Article 107033. https://doi.org/10.1016/j.asoc.2020.107033

© 2020 Learning from imbalanced datasets is highly demanded in real-world applications and a challenge for standard classifiers that tend to be biased towards the classes with the majority of the examples. Undersampling approaches reduce the size of... Read More about EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification.

Decomposition-Fusion for Label Distribution Learning (2020)
Journal Article
González, M., González-Almagro, G., Triguero, I., Cano, J.-R., & García, S. (2021). Decomposition-Fusion for Label Distribution Learning. Information Fusion, 66, 64-75. https://doi.org/10.1016/j.inffus.2020.08.024

Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than to a single label or multiple labels. Current LDL methods have proven their effectiveness in many real-life... Read More about Decomposition-Fusion for Label Distribution Learning.

Redundancy and Complexity Metrics for Big Data Classification: Towards Smart Data (2020)
Journal Article
Maillo, J., Triguero, I., & Herrera, F. (2020). Redundancy and Complexity Metrics for Big Data Classification: Towards Smart Data. IEEE Access, 1-1. https://doi.org/10.1109/access.2020.2991800

It is recognized the importance of knowing the descriptive properties of a dataset when tackling a data science problem. Having information about the redundancy, complexity and density of a problem allows us to make decisions as to which data preproc... Read More about Redundancy and Complexity Metrics for Big Data Classification: Towards Smart Data.

Multigranulation Super-Trust Model for Attribute Reduction (2020)
Journal Article
Ding, W., Pedrycz, W., Triguero, I., Cao, Z., & Lin, C.-T. (2020). Multigranulation Super-Trust Model for Attribute Reduction. IEEE Transactions on Fuzzy Systems, 29(6), 1395-1408. https://doi.org/10.1109/tfuzz.2020.2975152

As big data often contains a significant amount of uncertain, unstructured, and imprecise data that are structurally complex and incomplete, traditional attribute reduction methods are less effective when applied to large-scale incomplete information... Read More about Multigranulation Super-Trust Model for Attribute Reduction.

Fast and Scalable Approaches to Accelerate the Fuzzy k Nearest Neighbors Classifier for Big Data (2019)
Journal Article
Maillo, J., García, S., Luengo, J., Herrera, F., & Triguero, I. (2020). Fast and Scalable Approaches to Accelerate the Fuzzy k Nearest Neighbors Classifier for Big Data. IEEE Transactions on Fuzzy Systems, 28(5), 874-886. https://doi.org/10.1109/TFUZZ.2019.2936356

One of the best-known and most effective methods in supervised classification is the k nearest neighbors algorithm (kNN). Several approaches have been proposed to improve its accuracy, where fuzzy approaches prove to be among the most successful, hig... Read More about Fast and Scalable Approaches to Accelerate the Fuzzy k Nearest Neighbors Classifier for Big Data.

Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study (2019)
Journal Article
Canizo, M., Triguero, I., Conde, A., & Onieva, E. (2019). Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study. Neurocomputing, 363, 246-260. https://doi.org/10.1016/j.neucom.2019.07.034

Detecting anomalies in time series data is becoming mainstream in a wide variety of industrial applications in which sensors monitor expensive machinery. The complexity of this task increases when multiple heterogeneous sensors provide information of... Read More about Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study.

A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective (2019)
Journal Article
Angarita-Zapata, J. S., Masegosa, A. D., & Triguero, I. (2019). A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective. IEEE Access, 7, 68185 -68205. https://doi.org/10.1109/ACCESS.2019.2917228

One contemporary policy to deal with traffic congestion is the design and implementation of forecasting methods that allow users to plan ahead of time and decision makers to improve traffic management. Current data availability and growing computatio... Read More about A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective.

Virtual porous materials to predict the air void topology and hydraulic conductivity of asphalt roads (2019)
Journal Article
Aboufoul, M., Chiarelli, A., Triguero, I., & Garcia, A. (2019). Virtual porous materials to predict the air void topology and hydraulic conductivity of asphalt roads. Powder Technology, 352, 294-304. https://doi.org/10.1016/j.powtec.2019.04.072

This paper investigates the effects of air void topology on hydraulic conductivity in asphalt mixtures with porosity in the range 14%–31%. Virtual asphalt pore networks were generated using the Intersected Stacked Air voids (ISA) method, with its par... Read More about Virtual porous materials to predict the air void topology and hydraulic conductivity of asphalt roads.

A review on the self and dual interactions between machine learning and optimisation (2019)
Journal Article
Song, H., Triguero, I., & Özcan, E. (2019). A review on the self and dual interactions between machine learning and optimisation. Progress in Artificial Intelligence, 8(2), 143–165. https://doi.org/10.1007/s13748-019-00185-z

Machine learning and optimisation are two growing fields of artificial intelligence with an enormous number of computer science applications. The techniques in the former area aim to learn knowledge from data or experience, while the techniques from... Read More about A review on the self and dual interactions between machine learning and optimisation.

PAS3-HSID: a Dynamic Bio-Inspired Approach for Real-Time Hot Spot Identification in Data Streams (2019)
Journal Article
Tickle, R., Triguero, I., Figueredo, G. P., Mesgarpour, M., & John, R. I. (2019). PAS3-HSID: a Dynamic Bio-Inspired Approach for Real-Time Hot Spot Identification in Data Streams. Cognitive Computation, 11(3), 434–458. https://doi.org/10.1007/s12559-019-09638-y

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Hot spot identification is a very relevant problem in a wide variety of areas such as health care, energy or transportation. A hot spot is defined as a region of high likelihood o... Read More about PAS3-HSID: a Dynamic Bio-Inspired Approach for Real-Time Hot Spot Identification in Data Streams.

Handling uncertainty in citizen science data: towards an improved amateur-based large-scale classification (2018)
Journal Article
Jiménez, M., Triguero, I., & John, R. (2019). Handling uncertainty in citizen science data: towards an improved amateur-based large-scale classification. Information Sciences, 479, 301-320. https://doi.org/10.1016/j.ins.2018.12.011

© 2018 Citizen Science, traditionally known as the engagement of amateur participants in research, is showing great potential for large-scale processing of data. In areas such as astronomy, biology, or geo-sciences, where emerging technologies genera... Read More about Handling uncertainty in citizen science data: towards an improved amateur-based large-scale classification.

Transforming big data into smart data: An insight on the use of the k-nearest neighbors algorithm to obtain quality data (2018)
Journal Article
Triguero, I., Garcia-Gil, D., Maillo, J., Luengo, J., Garcia, S., & Herrera, F. (2019). Transforming big data into smart data: An insight on the use of the k-nearest neighbors algorithm to obtain quality data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(2), Article e1289. https://doi.org/10.1002/widm.1289

The k-nearest neighbours algorithm is characterised as a simple yet effective data mining technique. The main drawback of this technique appears when massive amounts of data -likely to contain noise and imperfections - are involved, turning this algo... Read More about Transforming big data into smart data: An insight on the use of the k-nearest neighbors algorithm to obtain quality data.

Coevolutionary fuzzy attribute order reduction with complete attribute-value space tree (2018)
Journal Article
Ding, W., Triguero, I., & Lin, C.-T. (2018). Coevolutionary fuzzy attribute order reduction with complete attribute-value space tree. IEEE Transactions on Emerging Topics in Computational Intelligence, https://doi.org/10.1109/tetci.2018.2869919

Since big data sets are structurally complex, high-dimensional, and their attributes exhibit some redundant and irrelevant information, the selection, evaluation, and combination of those large-scale attributes pose huge challenges to traditional met... Read More about Coevolutionary fuzzy attribute order reduction with complete attribute-value space tree.