Learning Multicriteria Fuzzy Classification Method PROAFTN from Data

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TypeArticle
ConferenceComputers and Operations Research Journal
IssueVolume 34, Issue 7, Pages 1885-1898
Subjectdata mining; multiple criteria classification; PROAFTN procedure; variable neighborhood search
AbstractIn 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.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48254
NPARC number8914339
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Record identifierce2dc2ef-277f-40ee-a2d5-13d4aa4211bc
Record created2009-04-22
Record modified2016-05-09
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