@conference {799,
	title = {E2PAMEA: un algoritmo evolutivo para la extracci{\'o}ni eficiente de patrones emergentes difusos en entornos big data},
	booktitle = {Proceedings of the XIX Conference of the Spanish Association for Artificial Intelligence},
	year = {2021},
	author = {A.M. Garc{\'\i}a-Vico and D. Elizondo and Francisco Charte and P. Gonz{\'a}lez and C. J. Carmona}
}
@conference {780,
	title = {A Preliminary Study on Crop Classification with Unsupervised Algorithms for Time Series on Images with Olive Trees and Cereal Crops },
	booktitle = {15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)},
	year = {2020},
	note = {PID2019-107793GB-I00},
	month = {08/2020},
	pages = {276-285},
	abstract = {Satellite imagery has been consolidated as an accurate option to monitor or classify crops. This is due to the continuous increase in spatial-temporal resolution and the availability of free access to this kind of services. In order to generate crop type maps (a valuable preprocessing step to most remote agriculture monitoring application), time series are built from remote sensing images, and supervised techniques are widely used to classify them. However, one of the main drawbacks of these methods is the lack of labelled data sets to carry out the training process. Unsupervised classification has been less frequently used in this research field. The paper presents an experimental study comparing traditional clustering algorithms (with different dissimilarity measures) for the classification of olive trees and cereal crops from time series remote sensing data. The results obtained provide crucial information for developing novel and more accurate crop mapping algorithms.},
	keywords = {Clustering Crop mappings, Satellite imagery, Time series classification, Unsupervised learning},
	doi = {https://doi.org/10.1007/978-3-030-57802-2_27},
	author = {A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and D. Elizondo and Lipika Deka and M. J. del Jesus}
}
@article {765,
	title = {E2PAMEA: A fast evolutionary algorithm for extracting fuzzy emerging patterns in big data environments},
	journal = {Neurocomputing},
	volume = {415},
	year = {2020},
	note = {TIN2015-68454-R; BES-2016-07773},
	month = {11/2020},
	pages = {60-73},
	abstract = {In this paper, a cooperative-competitive multi-objective evolutionary fuzzy system called E2PAMEA is presented for the extraction of emerging patterns in big data environments. E2PAMEA follows an adaptive schema to automatically employ different genetic operators according to the learning needs, which avoid the tuning of some parameters. It also employs a token-competition-based procedure for updating an elite population where the best set of patterns found so far is stored. In addition, a novel MapReduce procedure for an efficient computation of the evaluation function employed for guiding the search process is proposed. The method, called Bit-LUT employs a pre-evaluation stage where data is represented as a look-up table made of bit sets. This look-up table can be employed later in the chromosome evaluation by means of bitwise operations, reducing the computational complexity of the process.

The experimental study carried out shows that E2PAMEA is a promising alternative for the extraction of high-quality emerging patterns in big data. In addition, the proposed Bit-LUT evaluation shows a significant improvement on efficiency with a great scalability capacity on both dimensions of data, which enables the processing of massive datasets faster than other alternatives.},
	keywords = {Emerging pattern mining Evolutionary fuzzy systems Multi-objective evolutionary algorithm Big data},
	doi = {https://doi.org/10.1016/j.neucom.2020.07.007},
	author = {A.M. Garc{\'\i}a-Vico and Francisco Charte and P. Gonz{\'a}lez and D. Elizondo and C. J. Carmona}
}
@article {269,
	title = {Supervised Descriptive Rule Discovery: A Survey of the State-of-the-Art},
	year = {2016},
	pages = {1-11},
	author = {C. J. Carmona and D. Elizondo}
}
@article {542,
	title = {A fuzzy genetic programming-based algorithm for subgroup discovery and the application to one problem of pathogenesis of acute sore throat conditions in humans},
	journal = {Information Sciences},
	volume = {298},
	year = {2015},
	note = {TIN2012-33856},
	pages = {180-197},
	doi = {http://dx.doi.org/10.1016/j.ins.2014.11.030},
	author = {C. J. Carmona and V. Ruiz-Rodado and M. J. del Jesus and A. Weber and M. Grootveld and P. Gonz{\'a}lez and D. Elizondo}
}
@article {626,
	title = {Subgroup Discovery through Evolutionary Fuzzy Systems applied to Bioinformatics problems},
	year = {2015},
	pages = {1-28},
	institution = {De Montfort University},
	address = {Leicester, United Kingdom},
	author = {C. J. Carmona and D. Elizondo}
}
@article {624,
	title = {Analysing the Moodle e-learning platform through subgroup discovery algorithms based on evolutionary fuzzy systems},
	year = {2012},
	pages = {1-28},
	institution = {De Montfort University},
	address = {Leicester, United Kingdom},
	author = {C. J. Carmona and D. Elizondo}
}
@article {625,
	title = {Subgroup Discovery: Real-World Applications},
	year = {2011},
	pages = {1-21},
	institution = {De Montfort University},
	address = {Leicester, United Kingdom},
	author = {C. J. Carmona and D. Elizondo}
}
