Inducing Comprehensibility In Evolutionary Polynomial-Fuzzy Classification Models

Abstract

Comprehensibility is an important factor in medical predictive modelling as it dictates the credibility and even acceptability of a model. Generally, the performance of a model has always been the primary focus in most data mining jobs. Where there are serious risks posed by the decisions made by a model, it is not feasible to view comprehensibility aspects of a model as secondary to performance. While model comprehensibility is a topic that has aroused a lot of interest with two conference workshops (AI-UCAI'95 & AAAI 2005) placing it as its keynote issue and many papers written about it, there are no empirical methods of measuring it or even one consistent way to define it. It is generally accepted that smaller models are more comprehensible than larger ones. This forms the basis of most researches conducted in this area. In this paper, we investigate the efficacy of using multiobjective optimization in the Pareto sense to meet comprehensibility demands of models. Some of the objective functions used in this paper are novel while others have been used in other researches before. The results obtained show that incorporating aspects of comprehensibility in the induction process models does not necessarily retard the performance of models and could actually improve the performance versus complexity trade-off of evolutionary polynomial-fuzzy structures