A Threshold-free Classification Mechanism in Genetic Programming for High-dimensional Unbalanced Classification

Abstract

Class imbalance is an unavoidable issue in many real-world applications. Learning from unbalanced data, classifiers are often biased toward the majority class, while the minority class is important as well (even more important in many cases). How the issue of class imbalance is addressed becomes more challenging if a classification task further encounters the high dimensionality issue. This paper proposes a new genetic programming (GP) approach to high-dimensional unbalanced classification. A new classification mechanism is proposed for GP to improve its classification performance. This new classification mechanism is independent of a classification threshold to separate the majority class and the minority class. The effectiveness of the proposed method is examined on seven high-dimensional unbalanced datasets. Experimental results indicate that the proposed GP method often performs better than other GP methods that use a fitness function to solve the issue of class imbalance, in terms of classification performance and training time.