Learning multicriteria fuzzy classification method PROAFTN from data

Authors:
Nabil Belacel;Hiral Bhasker Raval;Abraham P. Punnen
Affiliations:
Institute for Information Technology-e-Business, National Research Council Canada, 127 Carleton Street, Saint John, NB, Canada E2L 2Z6 and Department of Mathematical Sciences, University of New Br ...;Institute for Information Technology-e-Business, National Research Council Canada, 127 Carleton Street, Saint John, NB, Canada E2L 2Z6 and Department of Mathematical Sciences, University of New Br ...;Department of Mathematics, Simon Fraser University, 13450 102nd AV, Surrey, British Columbia, Canada V3T 5X3
Venue:
Computers and Operations Research
Year:
2007

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Abstract

In this paper, we present a new methodology for learning parameters of multiple criteria classification method PROAFTN from data. There are numerous representations and techniques available for data mining, for example decision trees, rule bases, artificial neural networks, density estimation, regression and clustering. The PROAFTN method constitutes another approach for data mining. It belongs to the class of supervised learning algorithms and assigns membership degree of the alternatives to the classes. The PROAFTN method requires the elicitation of its parameters for the purpose of classification. Therefore, we need an automatic method that helps us to establish these parameters from the given data with minimum classification errors. Here, we propose variable neighborhood search metaheuristic for getting these parameters. The performances of the newly proposed method were evaluated using 10 cross validation technique. The results are compared with those obtained by other classification methods previously reported on the same data. It appears that the solutions of substantially better quality are obtained with proposed method than with these former ones.