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A heuristic approach to classifying labeled/unlabeled data sets / K. Y. Huang in Journal of the operational research society (JORS), Vol. 63 N° 9 (Septembre 2012) 

Public ISBD [article]  in Journal of the operational research society (JORS) > Vol. 63 N° 9 (Septembre 2012) . - pp. 1248–1257 Titre : A heuristic approach to classifying labeled/unlabeled data sets Type de document : texte imprimé Auteurs : K. Y. Huang, Auteur Année de publication : 2012 Article en page(s) : pp. 1248–1257 Note générale : Operational research Langues : Anglais (eng) Mots-clés : Optimization  Decision  analysis  Variable  precision  rough  set  MVPRS-index  method  Classification Index. décimale : 001.424 Résumé : A classification method, which comprises Fuzzy C-Means method, a modified form of the Huang-index function and Variable Precision Rough Set (VPRS) theory, is proposed for classifying labeled/unlabeled data sets in this study. This proposed method, designated as the MVPRS-index method, is used to partition the values of per conditional attribute within the data set and to achieve both the optimal number of clusters and the optimal accuracy of VPRS classification. The validity of the proposed approach is confirmed by comparing the classification results obtained from the MVPRS-index method for UCI data sets and a typical stock market data set with those obtained from the supervised neural networks classification method. Overall, the results show that the MVPRS-index method could be applied to data sets not only with labeled information but also with unlabeled information, and therefore provides a more reliable basis for the extraction of decision-making rules of labeled/unlabeled datasets. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v63/n9/abs/jors2011103a.html [article] A heuristic approach to classifying labeled/unlabeled data sets [texte imprimé] / K. Y. Huang, Auteur . - 2012 . - pp. 1248–1257. Operational research Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 63 N° 9 (Septembre 2012) . - pp. 1248–1257 Mots-clés : Optimization  Decision  analysis  Variable  precision  rough  set  MVPRS-index  method  Classification Index. décimale : 001.424 Résumé : A classification method, which comprises Fuzzy C-Means method, a modified form of the Huang-index function and Variable Precision Rough Set (VPRS) theory, is proposed for classifying labeled/unlabeled data sets in this study. This proposed method, designated as the MVPRS-index method, is used to partition the values of per conditional attribute within the data set and to achieve both the optimal number of clusters and the optimal accuracy of VPRS classification. The validity of the proposed approach is confirmed by comparing the classification results obtained from the MVPRS-index method for UCI data sets and a typical stock market data set with those obtained from the supervised neural networks classification method. Overall, the results show that the MVPRS-index method could be applied to data sets not only with labeled information but also with unlabeled information, and therefore provides a more reliable basis for the extraction of decision-making rules of labeled/unlabeled datasets. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v63/n9/abs/jors2011103a.html