Dr ISAAC TRIGUERO VELAZQUEZ's Outputs (8)

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.

KEEL 3.0: an open source software for multi-stage analysis in data mining (2017)
Journal Article
Triguero, I., González, S., Moyano, J. M., García, S., Alcalá-Fdez, J., Luengo, J., Fernández, A., del Jesús, M. J., Sánchez, L., & Herrera, F. (2017). KEEL 3.0: an open source software for multi-stage analysis in data mining. International Journal of Computational Intelligence Systems, 10(1), https://doi.org/10.2991/ijcis.10.1.82

This paper introduces the 3rd major release of the KEEL Software. KEEL is an open source Java framework (GPLv3 license) that provides a number of modules to perform a wide variety of data mining tasks. It includes tools to performdata management, des... Read More about KEEL 3.0: an open source software for multi-stage analysis in data mining.

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, August). Vehicle incident hot spots identification: An approach for big data. Presented at 2017 IEEE Trustcom/BigDataSE/ICESS, Sydney, NSW, Australia

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 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.

Self-labeling techniques for semi-supervised time series classification: an empirical study (2017)
Journal Article
González, M., Bergmeir, C., Triguero, I., Rodríguez, Y., & Benítez, J. M. (in press). Self-labeling techniques for semi-supervised time series classification: an empirical study. Knowledge and Information Systems, https://doi.org/10.1007/s10115-017-1090-9

An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised cla... Read More about Self-labeling techniques for semi-supervised time series classification: an empirical study.

Exact fuzzy k-Nearest neighbor classification for big datasets (2017)
Presentation / Conference Contribution
Maillo, J., Luengo, J., García, S., Herrera, F., & Triguero, I. Exact fuzzy k-Nearest neighbor classification for big datasets. Presented at IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017)

The k-Nearest Neighbors (kNN) classifier is one of the most effective methods in supervised learning problems. It classifies unseen cases comparing their similarity with the training data. Nevertheless, it gives to each labeled sample the same import... Read More about Exact fuzzy k-Nearest neighbor classification for big datasets.

A first attempt on global evolutionary undersampling for imbalanced big data (2017)
Presentation / Conference Contribution
Triguero, I., Galar, M., Bustince, H., & Herrera, F. A first attempt on global evolutionary undersampling for imbalanced big data. Presented at IEEE Congress on Evolutionary Computation (CEC 2017)

The design of efficient big data learning models has become a common need in a great number of applications. The massive amounts of available data may hinder the use of traditional data mining techniques, especially when evolutionary algorithms are i... Read More about A first attempt on global evolutionary undersampling for imbalanced big data.

Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection (2017)
Journal Article
Peralta, D., Triguero, I., García, S., Saeys, Y., Benitez, J. M., & Herrera, F. (2017). Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection. Knowledge-Based Systems, 126, https://doi.org/10.1016/j.knosys.2017.03.014

Fingerprint recognition has been a hot research topic along the last few decades, with many applications and ever growing populations to identify. The need of flexible, fast identification systems is therefore patent in such situations. In this conte... Read More about Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection.