1 Ocak 2014 Dergi makalesi Açık Erişim

Yildiz, Olcay Taner

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    <subfield code="a">In many pattern recognition applications, feature space expansion is a key step for improving the performance of the classifier. In this paper, we (i) expand the discrete feature space by generating all orderings of values of k discrete attributes exhaustively, (ii) modify the well-known decision tree and rule induction classifiers (ID3, Quilan, 1986 [1] and Ripper, Cohen, 1995 [2]) using these orderings as the new attributes. Our simulation results on 15 datasets from UCI repository [3] show that the novel classifiers perform better than the proper ones in terms of error rate and complexity. (C) 2013 Elsevier Ltd. All rights reserved.</subfield>
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    <subfield code="a">On the feature extraction in discrete space</subfield>
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