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

• URL: http://arxiv.org/abs/2105.04009v1
• Date: Sun, 9 May 2021 19:47:45 GMT
• Title: RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification
• Authors: Micha{\l} Koziarski, Colin Bellinger, Micha{\l} Wo\'zniak
• Abstract summary: Resampling the training data is the standard approach to improving classification performance on imbalanced binary data. RB- CCR exploits the class potential to accurately locate sub-regions of the data-space for synthetic oversampling. Our results 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.
• Score: 5.448684866061922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $5\times2$ 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.

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