Discovering Knowledge Rules with Multi-Objective Evolutionary Computing

Most Machine Learning systems target into inducing classifiers with optimal coverage and precision measures. Although this constitutes a good approach for prediction, it might not provide good results when the user is more interested in description. In this case, the induced models should present other properties such as novelty, interestingness and so forth. In this paper we present a research work based in Multi-Objective Evolutionary Computing to construct individual knowledge rules targeting arbitrary user-defined criteria via objective quality measures such as precision, support, novelty etc. This paper also presents a comparison among multi-objective and ranking composition techniques. It is shown that multi-objective-based methods attain better results than ranking-based methods, both in terms of solution dominance and diversity of solutions in the Pareto front.