Dr ISAAC TRIGUERO VELAZQUEZ I.TrigueroVelazquez@nottingham.ac.uk
ASSOCIATE PROFESSOR
KEEL 3.0: an open source software for multi-stage analysis in data mining
Triguero, Isaac; Gonz�lez, Sergio; Moyano, Jose M.; Garc�a, Salvador; Alcal�-Fdez, Jes�s; Luengo, Julian; Fern�ndez, Alberto; del Jes�s, Maria Jos�; S�nchez, Luciano; Herrera, Francisco
Authors
Sergio Gonz�lez
Jose M. Moyano
Salvador Garc�a
Jes�s Alcal�-Fdez
Julian Luengo
Alberto Fern�ndez
Maria Jos� del Jes�s
Luciano S�nchez
Francisco Herrera
Abstract
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, design of multiple kind of experiments, statistical analyses, etc. This framework also contains KEEL-dataset, a data repository for multiple learning tasks featuring data partitions and algorithms’ results over these problems. In this work, we describe the most recent components added to KEEL 3.0, including new modules for semi-supervised learning, multi-instance learning, imbalanced classification and subgroup discovery. In addition, a new interface in R has been incorporated to execute algorithms included in KEEL. These new features greatly improve the versatility of KEEL to deal with more modern data mining problems.
Citation
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
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 9, 2017 |
Publication Date | Sep 26, 2017 |
Deposit Date | Sep 14, 2017 |
Publicly Available Date | Sep 26, 2017 |
Journal | International Journal of Computational Intelligence Systems |
Print ISSN | 1875-6891 |
Electronic ISSN | 1875-6883 |
Publisher | Taylor and Francis |
Peer Reviewed | Peer Reviewed |
Volume | 10 |
Issue | 1 |
DOI | https://doi.org/10.2991/ijcis.10.1.82 |
Keywords | Open Source, Java, Data Mining, Preprocessing, Evolutionary Algorithms |
Public URL | https://nottingham-repository.worktribe.com/output/884772 |
Publisher URL | http://www.atlantis-press.com/journals/ijcis/25883592 |
Contract Date | Sep 14, 2017 |
Files
ijcis_10_1238_1249 (1).pdf
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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc/4.0
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