On the feature extraction in discrete space

被引：2
|
作者：
Yildiz, Olcay Taner [1 ]
机构：
[1] Isik Univ, Dept Comp Engn, TR-34980 Istanbul, Turkey
来源：
PATTERN RECOGNITION | 2014年 / 47卷 / 05期
关键词：
Feature extraction; Discrete space; Decision tree induction; Rule induction; DECISION TREES;
D O I：
10.1016/j.patcog.2013.11.023
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：1988 / 1993
页数：6
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