Hvbrid rule-extraction from support vector machines

被引：0

作者：

Diederich, J ^{[1
]}

Barakat, N ^{[1
]}

机构：

[1] Sohar Univ, Fac Sci Appl, Sohar, Oman

来源：

2004 IEEE CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS, VOLS 1 AND 2 | 2004年

关键词：

data mining; hybrid computational intelligence algorithms; rule-extraction and explanation;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Rule-extraction from artificial neural networks (ANNs) as well as support vector machines (SVMs) provide explanations for the decisions made by these systems. This explanation capability is very important in applications such as medical diagnosis. Over the last decade, a multitude of algorithms for rule-extraction from ANNs have been developed. However, rule-extraction from SVMs is not widely available yet. In this paper, a hybrid approach for rule-extraction from SVMs is outlined. This approach has two basic components: (1) data reduction using a logistic regression model and (2) learning based rule-extraction. The quality of the extracted rules is then evaluated in terms of fidelity, accuracy, consistency and comprehensibitity. The rules are also verified against the available knowledge from the domain problem (diabetes) to assure correctness and validity.

引用

页码：1271 / 1276

页数：6

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