A Novel Multi-class Classification Architecture Combining Population-based Sampling and Multi-expert Classifier for Imbalanced Data

Abstract

Training a classifier based on imbalanced data set is considered a great challenge in classification tasks, as classifiers are often "biased" due to highly skewed data distribution and overlapping borderline between different classes. When the imbalanced data appears in the multi-class classification scenario, the classification difficulty increases exponentially. In this paper, we propose an integrated approach to handle imbalanced multi-class classification by combining the population-based sampling method and a multi-expert classifier. In the implementation, we choose the Ant Colony Optimization to realize the sampling process. As for the classifier, the voting mechanism is applied to intensify the weak classifiers. To test the algorithm’s performance, we choose 10 representative imbalanced multi-class data sets from the UCI Machine Learning Repository. G – mean and mAUC are chosen as the metrics. According to the experimental results, the proposed algorithm dominates in 8 data sets and gets a second place in 1 data set when evaluated by G – mean, and ranks first in 3 data sets and top-3 among the most for mAUC.