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- W2054369317 abstract "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." @default.
- W2054369317 created "2016-06-24" @default.
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- W2054369317 date "2014-05-01" @default.
- W2054369317 modified "2023-09-25" @default.
- W2054369317 title "On the feature extraction in discrete space" @default.
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- W2054369317 doi "https://doi.org/10.1016/j.patcog.2013.11.023" @default.
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