A Cost-sensitive Genetic Programming Approach for High-dimensional Unbalanced Classification

Abstract

Many real-world classification problems encounter the problem of class imbalance. Genetic programming (GP) is one of the most important evolutionary algorithms, making crucial contributions to classification, particularly in the high-dimensional case. However, like other classification algorithms, GP may also develop biased classifiers when the class distribution is unbalanced or skewed. This is because standard GP treats each instance equally and assumes the same cost of different misclassification. In unbalanced classification, misclassification cost of the minority class is often serious than that of the majority class. Cost-sensitive learning has been successfully applied to solve the problem of class imbalance for many classification algorithms, but it has not been heavily investigated in GP. This paper investigates how cost-sensitive learning can be effectively used by GP to address the problem of class imbalance in high-dimensional unbalanced classification. Experimental results on six high-dimensional unbalanced datasets show the better performance of the proposed methods than the compared methods.