Imbalanced Data Classification Based on Hybrid Methods

Imbalanced data are ubiquitous in real-world datasets. This study investigate imbalanced data distribution for binary classification, i.e., where the number of majority class instances is significantly greater than the number of minority class instances. It is assumed that traditional machine learning algorithms attempt to minimize empirical risk factors, and, as a result, the classification accuracy of the minority is often sacrificed. However, people are often interested in the minority. Various data-level methods, such as over-and under-sampling, and algorithm-level methods, such as ensemble, cost-sensitive, and one-class learning, have been proposed to improve classifier performance with an imbalanced data distribution. Based on such methods, this study proposed a hybrid approach to deal with imbalanced data problem that comprises data preprocessing, clustering, data balancing, model building, and ensemble.