Machine Learning of Hybrid Classification Models for Decision Support

Machine learning methods used for decision support must achieve (a) high accuracy of decisions they recommend, and (b) deep understanding of decisions, so decision makers could trust them. Methods for learning implicit, non-symbolic knowledge provide better predictive accuracy. Methods for learning explicit, symbolic knowledge produce more comprehensible models. Hybrid machine learning models combine strengths of both knowledge representation model types. In this paper we compare predictive accuracy and comprehensibility of explicit, implicit, and hybrid machine learning models for several standard medical diagnostics, electronic commerce, e-marketing and financial decision making problems. Their applicability in different environments desktop, mobile and cloud computing is briefly analyzed. Machine learning methods from Weka and R/Revolution environments are used.