A New Classifier Based on the Dual Indiscernibility Matrix

A new approach to classifier synthesis was proposed by Polkowski and in this work we propose an implementation of this idea. The idea is based on usage of a dual indiscernibility matrix which allows to determine for each test object in the data, pairs of training objects which cover in a sense the given test object. A family of pairs best covering the given object pass their decisions for majority voting on decision for the test object. We present results obtained by our classifier on standard data from UCI Repository and compare them with results obtained by means of k-NN and Bayes classifiers. The results are validated by multiple cross-validation. We find our classifier on par with k-NN and Bayes classifiers.

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