RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification

• Published in: Machine learning Vol. 110; no. 11-12; pp. 3059 - 3093
• Main Authors: Koziarski, Michał, Bellinger, Colin, Woźniak, Michał
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Binary data; Classification; Cleaning; Computer Science; Control; Machine Learning; Mechatronics; Natural Language Processing (NLP); Oversampling; Recall; Resampling; Robotics; Simulation and Modeling; Special Issue: Foundations of Data Science; Training; Imbalanced data; Radial basis functions; Oversampling; Machine learning; Classification
• ISSN: 0885-6125; 1573-0565
• DOI: 10.1007/s10994-021-06012-8

Real-world classification domains, such as medicine, health and safety, and finance, often exhibit imbalanced class priors and have asynchronous misclassification costs. In such cases, the classification model must achieve a high recall without significantly impacting precision. Resampling the training data is the standard approach to improving classification performance on imbalanced binary data. However, the state-of-the-art methods ignore the local joint distribution of the data or correct it as a post-processing step. This can causes sub-optimal shifts in the training distribution, particularly when the target data distribution is complex. In this paper, we propose Radial-Based Combined Cleaning and Resampling (RB-CCR). RB-CCR utilizes the concept of class potential to refine the energy-based resampling approach of CCR. In particular, RB-CCR exploits the class potential to accurately locate sub-regions of the data-space for synthetic oversampling. The category sub-region for oversampling can be specified as an input parameter to meet domain-specific needs or be automatically selected via cross-validation. Our 5 × 2 cross-validated results on 57 benchmark binary datasets with 9 classifiers show that RB-CCR achieves a better precision-recall trade-off than CCR and generally out-performs the state-of-the-art resampling methods in terms of AUC and G-mean.