KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework

نویسندگان

  • Jesús Alcalá-Fdez
  • Alberto Fernández
  • Julián Luengo
  • Joaquín Derrac
  • Salvador García
چکیده

The aim of this paper is to present three new aspects of KEEL: KEEL-dataset, 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.

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عنوان ژورنال:
  • Multiple-Valued Logic and Soft Computing

دوره 17  شماره 

صفحات  -

تاریخ انتشار 2011