Characterizing the applicability of classification algorithms using meta-level learning

Abstract

This paper is concerned with a comparative study of different machine learning, statistical and neural algorithms and an automatic analysis of test results. It is shown that machine learning methods themselves can be used in organizing this knowledge. Various datasets can be characterized using different statistical and information theoretic measures. These together with the test results can be used by a ML system to generate a set of rules which could also be altered or edited by the user. The system can be applied to a new dataset to provide the user with a set of recommendations concerning the suitability of different algorithms and these are graded by an appropriate information score. The experiments with the implemented system indicate that the method is viable and useful.