Combined weighted multi-objective optimizer for instance reduction in two-class imbalanced data problem

Abstract

Instance reduction from class-balanced data has been investigated in much research. However, there is a lack of studies on class-imbalanced data. Learning from imbalanced data lately has attracted a lot of attention due to the practical applications. In the case of two-class imbalanced data, the instances from one class, majority class, are more numerous than the instances from the other class, which is a minority class. The present paper aims to introduce a new instance reduction method that preserves between-class distributions in the balanced data and handles minority class instance reduction in two-class imbalanced data, efficiently. The proposed method solves the instance reduction issue from an unconstrained multi-objective optimization problem aspect. Accordingly, a new combined weighted optimizer is designed. By employing the chaotic krill herd evolutionary algorithm, both the minority and majority class spaces with the accelerated convergence are explored. Through this method, the original data set is purged of those instances that decrease accuracy, and Gmean. The performance has been evaluated on both imbalanced and balanced data sets collected from the UCI repository by the 10-fold cross-validation method. Evaluations show that the proposed method outperforms state-of-the-art methods in terms of classification accuracy, Gmean, reduction rates, and computational time.