Application of Genetic Programming to Induction of Linear Classification Trees

Abstract

. A common problem in datamining is to find accurate classifiers for a dataset. For this purpose, genetic programming (GP) is applied to a set of benchmark classification problems. Using GP we are able to induce decision trees with a linear combination of variables in each function node. A new representation of decision trees using strong typing in GP is introduced. With this representation it is possible to let the GP classify into any number of classes. Results indicate that GP can be applied successfully to classification problems. Comparisons with current state-of-the-art algorithms in machine learning are presented and areas of future research are identified. 1 Introduction Classification problems form an important area in datamining. For example, a bank may want to classify its clients in good and bad credit risks or a doctor may want to classify his patients as having diabetes or not. Classifiers may take the form of decision trees [11] (see Figure 1). In each node, a..