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

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., …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 & Francis Open
• 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