Dr ISAAC TRIGUERO VELAZQUEZ's Outputs (57)

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.

Fuzzy Hot Spot Identification for Big Data: An Initial Approach (2019)
Presentation / Conference Contribution
Triguero, I., Tickle, R., Figueredo, G. P., Mesgarpour, M., Ozcan, E., & John, R. I. (2019, June). Fuzzy Hot Spot Identification for Big Data: An Initial Approach. Presented at 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA

Hot spot identification problems are present across a wide range of areas, such as transportation, health care and energy. Hot spots are locations where a certain type of event occurs with high frequency. A recent big data approach is capable of iden... Read More about Fuzzy Hot Spot Identification for Big Data: An Initial Approach.

A Preliminary Approach for the Exploitation of Citizen Science Data for Fast and Robust Fuzzy k-Nearest Neighbour Classification (2019)
Presentation / Conference Contribution
Jimenez, M., Torres, M. T., John, R., & Triguero, I. (2019, June). A Preliminary Approach for the Exploitation of Citizen Science Data for Fast and Robust Fuzzy k-Nearest Neighbour Classification. Presented at 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA

Citizen science is becoming mainstream in a wide variety of real-world applications in astronomy or bioinformatics, in which, for example, classification tasks by experts are very time consuming. These projects engage amateur volunteers that are task... Read More about A Preliminary Approach for the Exploitation of Citizen Science Data for Fast and Robust Fuzzy k-Nearest Neighbour Classification.

Evolving Deep CNN-LSTMs for Inventory Time Series Prediction (2019)
Presentation / Conference Contribution
Xue, N., Triguero, I., Figueredo, G. P., & Landa-Silva, D. (2019, June). Evolving Deep CNN-LSTMs for Inventory Time Series Prediction. Presented at 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand

Inventory forecasting is a key component of effective inventory management. In this work, we utilise hybrid deep learning models for inventory forecasting. According to the highly nonlinear and non-stationary characteristics of inventory data, the mo... Read More about Evolving Deep CNN-LSTMs for Inventory Time Series Prediction.

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.

A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food (2019)
Presentation / Conference Contribution
Xue, N., Landa-Silva, D., Figueredo, G. P., & Triguero, I. (2019, February). A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food. Presented at 8th International Conference on Operations Research and Enterprise Systems, Prague, Czech Republic

The taste and freshness of perishable foods decrease dramatically with time. Effective inventory management requires understanding of market demand as well as balancing customers needs and references with products’ shelf life. The objective is to av... Read More about A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food.

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.

A preliminary study on Hybrid Spill-Tree Fuzzy k-Nearest Neighbors for big data classification (2018)
Presentation / Conference Contribution
Maillo, J., Luengo, J., Garcia, S., Herrera, F., & Triguero, I. (2018, July). A preliminary study on Hybrid Spill-Tree Fuzzy k-Nearest Neighbors for big data classification. Presented at 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Rio de Janeiro, Brazil

The Fuzzy k Nearest Neighbor (Fuzzy kNN) classifier is well known for its effectiveness in supervised learning problems. kNN classifies by comparing new incoming examples with a similarity function using the samples of the training set. The fuzzy ver... Read More about A preliminary study on Hybrid Spill-Tree Fuzzy k-Nearest Neighbors for big data classification.

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.

A Preliminary Study of the Feasibility of Global Evolutionary Feature Selection for Big Datasets under Apache Spark (2018)
Presentation / Conference Contribution
Galar, M., Triguero, I., Bustince, H., & Herrera, F. (2018, July). A Preliminary Study of the Feasibility of Global Evolutionary Feature Selection for Big Datasets under Apache Spark. Presented at 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil

Designing efficient learning models capable of dealing with tons of data has become a reality in the era of big data. However, the amount of available data is too much for traditional data mining techniques to be applicable. This issue is even more s... Read More about A Preliminary Study of the Feasibility of Global Evolutionary Feature Selection for Big Datasets under Apache Spark.

A genetic algorithm with composite chromosome for shift assignment of part-time employees (2018)
Presentation / Conference Contribution
Xue, N., Landa-Silva, D., Triguero, I., & Figueredo, G. P. A genetic algorithm with composite chromosome for shift assignment of part-time employees. Presented at 2018 IEEE Congress in Evolutionary Computation (IEEE CEC 2018)

Personnel scheduling problems involve multiple tasks, including assigning shifts to workers. The purpose is usually to satisfy objectives and constraints arising from management, labour unions and employee preferences. The shift assignment problem is... Read More about A genetic algorithm with composite chromosome for shift assignment of part-time employees.

A first approach for handling uncertainty in citizen science (2018)
Presentation / Conference Contribution
Jiménez, M., Triguero, I., & John, R. (2018, July). A first approach for handling uncertainty in citizen science. Paper presented at IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2018)

Citizen Science is coming to the forefront of scientific research as a valuable method for large-scale processing of data. New technologies in fields such as astronomy or bio-sciences generate tons of data, for which a thorough expert analysis is no... Read More about A first approach for handling uncertainty in citizen science.

Instance reduction for one-class classification (2018)
Journal Article
Krawczyk, B., Triguero, I., García, S., Woźniak, M., & Herrera, F. (in press). Instance reduction for one-class classification. Knowledge and Information Systems, https://doi.org/10.1007/s10115-018-1220-z

Instance reduction techniques are data preprocessing methods originally developed to enhance the nearest neighbor rule for standard classification. They reduce the training data by selecting or generating representative examples of a given problem. T... Read More about Instance reduction for one-class classification.

A preliminary study on automatic algorithm selection for short-term traffic forecasting (2018)
Presentation / Conference Contribution
Angarita-Zapata, J. S., Triguero, I., & Masegosa, A. D. (2018, October). A preliminary study on automatic algorithm selection for short-term traffic forecasting. Presented at 12th International Symposium on Intelligent Distributed Computing (IDC 2018), Bilbao, Spain

© 2018, Springer Nature Switzerland AG. Despite the broad range of Machine Learning (ML) algorithms, there are no clear baselines to find the best method and its configuration given a Short-Term Traffic Forecasting (STTF) problem. In ML, this is know... Read More about A preliminary study on automatic algorithm selection for short-term traffic forecasting.

On the use of convolutional neural networks for robust classification of multiple fingerprint captures (2017)
Journal Article
Peralta, D., Triguero, I., García, S., Saeys, Y., Benitez, J. M., & Herrera, F. (in press). On the use of convolutional neural networks for robust classification of multiple fingerprint captures. International Journal of Intelligent Systems, 33(1), https://doi.org/10.1002/int.21948

Fingerprint classification is one of the most common approaches to accelerate the identification in large databases of fingerprints. Fingerprints are grouped into disjoint classes, so that an input fingerprint is compared only with those belonging to... Read More about On the use of convolutional neural networks for robust classification of multiple fingerprint captures.