A complete fuzzy decision tree technique

被引：297
作者：
Olaru, C [1 ]
Wehenkel, L [1 ]
机构：
[1] Univ Liege, Inst Montefiore, Dept Elect Engn & Comp Sci, B-4000 Liege, Belgium
来源：
FUZZY SETS AND SYSTEMS | 2003年 / 138卷 / 02期
关键词：
learning; approximate reasoning; fuzzy decision tree; data mining; soft split; pruning; global optimization; regression tree; neural network;
D O I：
10.1016/S0165-0114(03)00089-7
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper, a new method of fuzzy decision trees called soft decision trees (SDT) is presented. This method combines tree growing and pruning, to determine the structure of the soft decision tree, with refitting and backfitting, to improve its generalization capabilities. The method is explained and motivated and its behavior is first analyzed empirically on 3 large databases in terms of classification error rate, model complexity and CPU time. A comparative study on 11 standard UCI Repository databases then shows that the soft decision trees produced by this method are significantly more accurate than standard decision trees. Moreover, a global model variance study shows a much lower variance for soft decision trees than for standard trees as a direct cause of the improved accuracy. (C) 2003 Elsevier B.V. All rights reserved.
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收藏
页码：221 / 254
页数：34
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