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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 <jats:inline-formula><jats:alternatives><jats:tex-math>$$5\\times 2$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>5<\/mml:mn>\n                    <mml:mo>\u00d7<\/mml:mo>\n                    <mml:mn>2<\/mml:mn>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> 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.<\/jats:p>","DOI":"10.1007\/s10994-021-06012-8","type":"journal-article","created":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T17:03:31Z","timestamp":1634231011000},"page":"3059-3093","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification"],"prefix":"10.1007","volume":"110","author":[{"given":"Micha\u0142","family":"Koziarski","sequence":"first","affiliation":[]},{"given":"Colin","family":"Bellinger","sequence":"additional","affiliation":[]},{"given":"Micha\u0142","family":"Wo\u017aniak","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,14]]},"reference":[{"issue":"2\u20133","key":"6012_CR1","first-page":"255","volume":"17","author":"J Alcal\u00e1-Fdez","year":"2011","unstructured":"Alcal\u00e1-Fdez, J., Fern\u00e1ndez, A., Luengo, J., Derrac, J., & Garc\u00eda, S. 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