KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining
- https://doi.org/10.2991/ijcis.10.1.82How to use a DOI?
- Open Source, Java, Data Mining, Preprocessing, Evolutionary Algorithms
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.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).
Cite this article
TY - JOUR AU - Isaac Triguero AU - Sergio González AU - Jose M. Moyano AU - Salvador García AU - Jesús Alcalá-Fdez AU - Julián Luengo AU - Alberto Fernández AU - Maria José del Jesús AU - Luciano Sánchez AU - Francisco Herrera PY - 2017 DA - 2017/09 TI - KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining JO - International Journal of Computational Intelligence Systems SP - 1238 EP - 1249 VL - 10 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.10.1.82 DO - https://doi.org/10.2991/ijcis.10.1.82 ID - Triguero2017 ER -