CCR: A combined cleaning and resampling algorithm for imbalanced data classification

作者：Michał Koziarski, Michał Woźniak · 发表于：International Journal of Applied Mathematics and Computer Science · 年份：2017 · DOI：10.1515/amcs-2017-0050 · 被引用次数：109 · 研究领域：Imbalanced Data Classification Techniques、Machine Learning and Data Classification、Electricity Theft Detection Techniques

Abstract Imbalanced data classification is one of the most widespread challenges in contemporary pattern recognition. Varying levels of imbalance may be observed in most real datasets, affecting the performance of classification algorithms. Particularly, high levels of imbalance make serious difficulties, often requiring the use of specially designed methods. In such cases the most important issue is often to properly detect minority examples, but at the same time the performance on the majority class cannot be neglected. In this paper we describe a novel resampling technique focused on proper detection of minority examples in a two-class imbalanced data task. The proposed method combines cleaning the decision border around minority objects with guided synthetic oversampling. Results of the conducted experimental study indicate that the proposed algorithm usually outperforms the conventional oversampling approaches, especially when the detection of minority examples is considered.