Logical rule extraction from data by maximum neural networks

T. Saito, Y. Takefuji

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

3 Citations (Scopus)

Abstract

In this paper, a new neural computing method to extract logical rules from the training data sets is proposed. Maximum neural networks are used to train the weight and the threshold of the multi-layered (feedforward) neural network (MLNN). The threshold and the weights of the MLNN are trained to be a logical function (AND/OR) with the multiple input. The maximum neural network constructs the logical function on the MLNN so that it is not necessary to extract rules from the trained MLNN. The proposed method was experimented for the classification problem, Monk's problem 1. Experimental results showed that the proposed method learned the correct rule in more than 40% success rate.

Original languageEnglish
Title of host publicationProceedings of the 2nd International Conference on Intelligent Processing and Manufacturing of Materials, IPMM 1999
EditorsMarcello M. Veiga, John A. Meech, Michael H. Smith, Steven R. LeClair
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages723-728
Number of pages6
ISBN (Electronic)0780354893, 9780780354890
DOIs
Publication statusPublished - 1999 Jan 1
Event2nd International Conference on Intelligent Processing and Manufacturing of Materials, IPMM 1999 - Honolulu, United States
Duration: 1999 Jul 101999 Jul 15

Publication series

NameProceedings of the 2nd International Conference on Intelligent Processing and Manufacturing of Materials, IPMM 1999
Volume2

Other

Other2nd International Conference on Intelligent Processing and Manufacturing of Materials, IPMM 1999
CountryUnited States
CityHonolulu
Period99/7/1099/7/15

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Materials Science (miscellaneous)

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