LAUSR

Genetic programming (GP) has been successfully applied to classification. However, GP may evolve biased classifiers when encountering the problem of class imbalance. These biased classifiers are often not reliable to be applied to some real-world applications. High dimensionality makes it more difficult for classifiers to effectively separate the majority class and the minority class. The use of GP to handle the joint effect of high dimensionality and class imbalance has not been heavily investigated. In this paper, we propose a GP approach to high-dimensional imbalanced classification, with the goals of increasing the classification performance as well as saving training time. To achieve this goal, a new fitness function is developed to solve the problem of class imbalance, and moreover, a strategy is proposed to reuse previous good GP individuals for improving efficiency. The proposed method is examined on ten high-dimensional imbalanced datasets. Experimental results show that, for high-dimensional imbalanced classification, the proposed method generally outperforms other GP methods and traditional classification algorithms using sampling methods to solve the problem of class imbalance.