Transactions of the Institute of Systems, Control and Information Engineers
Online ISSN : 2185-811X
Print ISSN : 1342-5668
Associative Classification Using the Extended Associatron
Akihiro KANAGAWAMitsue SENOOHiromitsu TAKAHASHI
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1999 Volume 12 Issue 4 Pages 212-219

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Abstract

This paper deals with classification problem, such as diagnosis, in which classes are defined by categorical forms. Supposing there are some sample data, and each datum has n kinds of characteristic values. The problem to classify these samples into given m classes based on their characteristic values has been discussed for a long period of time. However, classic methods including the discriminant analysis are difficult to be applied to actual problems, because the assumption of multivariate normal distribution and equality of variance-covariance matrices are needed.
In this paper we propose a classification method using the associatron as an associative memory machine. We extend the associatron so as to have stable three leveled outputs following in the steps of a method given by Kanagawa et al. Examples of diagnosis of liver disease and the problem of iris classification are demonstrated.

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