KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework
J. Alcala-Fdez, A. Fernandez, J. Luengo, J. Derrac, S. Garcia, L. Sanchez and F Herrera
This work is related to the KEEL1 (Knowledge Extraction based on Evolutionary Learning) tool, an open source software that supports data management and a designer of experiments. KEEL pays special attention to the implementation of evolutionary learning and soft computing based techniques for Data Mining problems including regression, classification, clustering, pattern mining and so on.
The aim of this paper is to present three new aspects of KEEL: KEELdataset, a data set repository which includes the data set partitions in the KEEL format and shows some results of algorithms in these data sets; some guidelines for including new algorithms in KEEL, helping the researchers to make their methods easily accessible to other authors and to compare the results of many approaches already included within the KEEL software; and a module of statistical procedures developed in order to provide to the researcher a suitable tool to contrast the results obtained in any experimental study. A case of study is given to illustrate a complete case of application within this experimental analysis framework.
Keywords: Data mining, Data set repository, evolutionary algorithms, java, knowledge extraction, machine learning