A Hybrid Sampling Approach for Imbalanced Binary and Multi-Class Data Using Clustering Analysis

Abstract

Unequal data distribution among different classes usually cause a class imbalance problem. Due to the class imbalance, the classification models become biased toward the majority class and misclassify the minority class. Class imbalance issue becomes more complex when it occurs in multi-class data. The most common method to handle the class imbalance is data resampling that involves either oversampling minority class instances or under-sampling majority class instances. In the case of undersampling, there is a chance of losing some crucial information, whereas over-sampling can cause an overfitting problem. Therefore, we propose a novel Cluster-based Hybrid Sampling for Imbalance Data (CBHSID) strategy to address these issues. We calculate the mean of the data observations based on the number of classes. CBHSID uses the calculated mean as a threshold value to segregate majority and minority classes. We apply affinity propagation cluster analysis to each class to create sub-clusters. We calculate the distance of each data item of sub-cluster using centroid mean. We remove data observations that are away from the center of sub-cluster during under-sampling. On the other hand, during the oversampling, we generate synthetic samples using data observations near to the center of sub-cluster. We compared our proposed approach with a few state-of-the-art data balancing methods on 12 binary and 4 multi-class benchmark datasets. Based on Geometric-Mean (G-Mean), Recall, and F1-score, our method outperformed the other compared methods on 14 datasets out of 16. We identified that CBHSID is suitable for addressing class imbalance issues in both binary and multi-class classifications. In the current state, we have only validated CBHSID on stationary data streams. Consequently, CBHSID can further be tested on non-stationary data streams in online learning environments.

Multi-Class Imbalanced Data Classification: A Systematic Mapping Study

“…Therefore, with this goal in mind, several class-imbalanced learning methods have been proposed. Typically, these can be divided into three primary categories: data-level, algorithm-level, and hybrid approaches [11]. Nevertheless, timely categorization requires further clarification.…”

“…Nevertheless, timely categorization requires further clarification. For instance, some articles define classification as a combination of oversampling and undersampling methods [5], while others describe it as combining data and algorithm-level techniques [11].…”