KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining

• Published in: International journal of computational intelligence systems Vol. 10; no. 1; pp. 1238 - 1249
• Main Authors: Triguero, Isaac, González, Sergio, Moyano, Jose M., García, Salvador, Alcalá-Fdez, Jesús, Luengo, Julián, Fernández, Alberto, del Jesús, Maria José, Sánchez, Luciano, Herrera, Francisco
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.01.2017; Springer Nature B.V; Springer
• Subjects: Algorithms; Cognitive tasks; Data management; Data Mining; Evolutionary Algorithms; Java; Machine learning; Modules; Open Source; Open source software; Preprocessing; Research Article; Software; Statistical analysis; Subgroups; Java; Data Mining; Evolutionary Algorithms; Open Source; Preprocessing
• ISSN: 1875-6891; 1875-6883; 1875-6883
• DOI: 10.2991/ijcis.10.1.82

This paper introduces the 3 rd 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 perform data 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.