Cost-sensitive classification with genetic programming

Cost-sensitive classification is an attractive topic in data mining. Although genetic programming (GP) technique has been applied to general classification, to our knowledge, it has not been exploited to address cost-sensitive classification in the literature, where the costs of misclassification errors are non-uniform. To investigate the applicability of GP to cost-sensitive classification, this paper first reviews the existing methods of cost-sensitive classification in data mining. We then apply GP to address cost-sensitive classification by means of two methods through: a) manipulating training data, and b) modifying the learning algorithm. In particular, a constrained genetic programming (CGP), a GP-based cost-sensitive classifier, has been introduced in this study. CGP is capable of building decision trees to minimize not only the expected number of errors, but also the expected misclassification costs through a novel constraint fitness function. CGP has been tested on the heart disease dataset and the German credit dataset from the UCI repository. Its efficacy with respect to cost has been demonstrated by comparisons with non-cost-sensitive learning methods and cost-sensitive learning methods in terms of the costs.