Multi-objective learning of accurate and comprehensible classifiers – a case study

Abstract. Accuracy and comprehensibility are two important classifier properties, however they are typically conflicting. Research in the past years has shown that Pareto-based multi-objective approach for solving this problem is preferred to the traditional single-objective approach. Multi-objective learning can be represented as search that starts either from an accurate classifier and modifies it in order to produce more comprehensible classifiers (e.g. extracting rules from ANNs) or the other way around: starts from a comprehensible classifier and modifies it to produce more accurate classifiers. This paper presents a case study of applying a recent algorithm for multi-objective learning of hybrid trees MOLHC in human activity recognition domain. Advantages of MOLHC for the user and limitations of the algorithm are discussed on a number of datasets from the UCI repository