A method for improving the accuracy of data mining classification algorithms

Authors:
Nikolaos Mastrogiannis;Basilis Boutsinas;Ioannis Giannikos
Affiliations:
Department of Business Administration, University of Patras, GR-265 00 Patras, Greece;Department of Business Administration, University of Patras, GR-265 00 Patras, Greece and University of Patras Artificial Intelligence Research Center (UPAIRC), GR-265 00 Patras, Greece;Department of Business Administration, University of Patras, GR-265 00 Patras, Greece
Venue:
Computers and Operations Research
Year:
2009

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Abstract

In this paper we introduce a method called CL.E.D.M. (CLassification through ELECTRE and Data Mining), that employs aspects of the methodological framework of the ELECTRE I outranking method, and aims at increasing the accuracy of existing data mining classification algorithms. In particular, the method chooses the best decision rules extracted from the training process of the data mining classification algorithms, and then it assigns the classes that correspond to these rules, to the objects that must be classified. Three well known data mining classification algorithms are tested in five different widely used databases to verify the robustness of the proposed method.