KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining Isaac Triguero 1 , Sergio Gonz ´ alez 2 , Jose M. Moyano 4 , Salvador Garc´ ıa 2 , Jes ´ us Alcal ´ a-Fdez 2 , Juli´ an Luengo 2 , Alberto Fern´ andez 2 , Maria Jos´ e del Jes ´ us 5 , Luciano S ´ anchez 3 , Francisco Herrera 2 1 School of Computer Science University of Nottingham, Jubilee Campus Nottingham NG8 1BB, United Kingdom E-mail: Isaac.Triguero@nottingham.ac.uk 2 Department of Computer Science and Artificial Intelligence University of Granada, Granada, Spain, 18071 3 Department of Computer Science University of Oviedo, Gij´ on, 33204, Spain 4 Department of Computer Science and Numerical Analysis University of Cordoba, 14071 Cordoba, Spain 5 Department of Computer Science University of Ja´ en, Ja´ en, Spain Abstract This paper introduces the 3 rd major release of the KEEL Software. KEEL is an open source Java frame- work (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. Keywords: Open Source, Java, Data Mining, Preprocessing, Evolutionary Algorithms. 1. Introduction Data Mining (DM) techniques 25 are widely used in a broad number of applications that go beyond the computer science field 41 . In order to ease the ac- cess to these models for people not directly related to computer science, many commercial and non- commercial software suites have been made avail- able. The majority of the former are commercially distributed (e.g. SPSS Clementine, Oracle Data Mining or KnowledgeSTUDIO), but there is still a good number of open source tools. Among the ex- isting open source applications, Workflow-based en- vironments allow us to visually chain a number of DM methods together in a pipeline. The most used DM apps of this kind are: Weka 18 , KNIME 1 and KEEL 2 . International Journal of Computational Intelligence Systems, Vol. 10 (2017) 1238–1249 ___________________________________________________________________________________________________________ 1238 Received 6 March 2017 Accepted 9 September 2017 Copyright © 2017, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).