Application of Genetic Programming to Induction of Linear Classi cation Trees

A common problem in datamining is to nd accurate classiiers for a dataset. For this purpose, genetic programming (GP) is applied to a benchmark of classiication problems. In particular, using GP we are able to induce decision trees with a linear combination of variables in each function node. The eeects of techniques as limited error tness, tness sharing Pareto scoring and domination Pareto scoring are evaluated. Results indicate that GP can be applied succesfully to classiication problems. Comparisons with current state-of-the-art algorithms in machine learning are presented and areas of future research are identiied.