Greedy rule generation from discrete data and its use in neural network rule extraction

Koichi Odajima, Yoichi Hayashi, Rudy Setiono

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes GRG (Greedy Rule Generation) algorithm for generating classification rules from a data set with discrete attributes. The algorithm is "greedy" in the sense that at every iteration, it searches for the best rule to generate. The criteria for the best rule include the number of samples that it covers, the number of attributes involved in the rule, and the size of the input subspace it covers. This method is applied for extracting rules from neural networks that have been trained and pruned for solving classification problems. Neural networks with one hidden layer are trained and the proposed GRG algorithm is applied to their discretized hidden unit activation values. Our results show that rule extraction with the GRG method produces rule sets that are more accurate and concise compared to those obtained by a decision tree method and an existing neural network rule extraction method.

Original languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
Pages1833-1839
Number of pages7
Publication statusPublished - 1 Dec 2006
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: 16 Jul 200621 Jul 2006

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Conference

ConferenceInternational Joint Conference on Neural Networks 2006, IJCNN '06
CountryCanada
CityVancouver, BC
Period16/07/0621/07/06

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Odajima, K., Hayashi, Y., & Setiono, R. (2006). Greedy rule generation from discrete data and its use in neural network rule extraction. In International Joint Conference on Neural Networks 2006, IJCNN '06 (pp. 1833-1839). [1716332] (IEEE International Conference on Neural Networks - Conference Proceedings).