Applying GMDH algorithm to extract rules from examples

Authors:
Koji Fujimoto;Sampei Nakabayashi
Affiliations:
Financial Engineering Group, Inc., Akasaka Yamada Bldg. 2-21-8 Akasaka, Minato-ku, Tokyo, 107-0052, Japan;Financial Engineering Group, Inc., Akasaka Yamada Bldg. 2-21-8 Akasaka, Minato-ku, Tokyo, 107-0052, Japan
Venue:
Systems Analysis Modelling Simulation - Special issue: Self-organising modelling and simulation
Year:
2003

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Abstract

This article reports a new approach to rule extraction method by using Group Method of Data Handling (GMDH) Algorithm in Data Mining area. The advantages of this method are (1) it accepts both categorical and continuous data at the same time, and (2) rules can be extracted easily from the generated model.We applied GMDH Algorithm to categorical data set of US congress voting records to extract rules. The correction rate of GMDH rules was 97.3% -- higher than Tsukimoto's method of rule extraction from Back-propagation neural network (81.0%). It was also higher than 97.0% of C4.5.