Extracting decision trees from trained neural networks

被引:63
|
作者
Krishnan, R [1 ]
Sivakumar, G [1 ]
Bhattacharya, P [1 ]
机构
[1] Indian Inst Technol, Dept Comp Sci & Engn, Bombay 400076, Maharashtra, India
关键词
rule extraction; decision trees; data mining; knowledge discovery; classification;
D O I
10.1016/S0031-3203(98)00181-2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper we present a methodology for extracting decision trees from input data generated from trained neural networks instead of doing it directly from the data. A genetic algorithm is used to query the trained network and extract prototypes. A prototype selection mechanism is then used to select a subset of the prototypes. Finally, a standard induction method like ID3 or C5.0 is used to extract the decision tree. The extracted decision trees can be used to understand the working of the neural network besides performing classification. This method is able to extract different decision trees of high accuracy and comprehensibility from the trained neural network. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:1999 / 2009
页数:11
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