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

• Main Authors: Isaac Triguero, Sergio González, Jose M. Moyano, Salvador García, Jesús Alcalá-Fdez, Julián Luengo, Alberto Fernández, Maria José del Jesús, Luciano Sánchez, Francisco Herrera
• Format: Article
• Language: English
• Published: Atlantis Press 2017-01-01
• Series: International Journal of Computational Intelligence Systems
• Subjects: Open Source; Java; Data Mining; Preprocessing; Evolutionary Algorithms
• Online Access: https://www.atlantis-press.com/article/25883592/view

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 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.