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Toward Optimal Feature Selection Using Ranking Methods and Classification Algorithms
Yugoslav journal of operations research, Tome 21 (2011) no. 1, p. 119 Cet article a éte moissonné depuis la source eLibrary of Mathematical Institute of the Serbian Academy of Sciences and Arts

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We presented a comparison between several feature ranking methods used on two real datasets. We considered six ranking methods that can be divided into two broad categories: statistical and entropy-based. Four supervised learning algorithms are adopted to build models, namely, IB1, Naive Bayes, C4.5 decision tree and the RBF network. We showed that the selection of ranking methods could be important for classification accuracy. In our experiments, ranking methods with different supervised learning algorithms give quite different results for balanced accuracy. Our cases confirm that, in order to be sure that a subset of features giving the highest accuracy has been selected, the use of many different indices is recommended.
Classification : 90B50, 62C99
Keywords: Feature selection, feature ranking methods, classification algorithms, classification accuracy. 
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     author = {Jasmina Novakovi\'c and Perica Strbac and Dusan Bulatovi\'c},
     title = {Toward {Optimal} {Feature} {Selection} {Using} {Ranking} {Methods} and {Classification} {Algorithms}},
     journal = {Yugoslav journal of operations research},
     pages = {119 },
     year = {2011},
     volume = {21},
     number = {1},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/YJOR_2011_21_1_a8/}
}
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Jasmina Novaković; Perica Strbac; Dusan Bulatović. Toward Optimal Feature Selection Using Ranking Methods and Classification Algorithms. Yugoslav journal of operations research, Tome 21 (2011) no. 1, p. 119 . http://geodesic.mathdoc.fr/item/YJOR_2011_21_1_a8/