Associative Classification Using the Extended Associatron

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.