Class Confidence Weighted kNN Algorithms for Imbalanced Data Sets

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Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, background): “intrinsic problems coming from application domains, such as the class imbalance problem [95], [96], and the class confidence problem; where objects are weighted regarding their confidence to all classes of the problem [97].”
A novel ensemble method for classifying imbalanced data (2015, background): “As most traditional classification algorithms, such as decision trees [10–12], k-nearest neighbors [13,14], and RIPPER [15,16], tend to generate models which maximize the overall classification accuracy, and the minority class is usually ignored [17–19].”
Adaptive Machine Learning for Credit Card Fraud Detection (2015, background): “In the family of lazy learning classifiers, Liu and Chawla [111] propose a kNN weighting strategy designed for handling the problem of class unbalance.”
Learning Deep Representation for Imbalanced Classification (2016, background): “Liu and Chawla [28] provide a weighting scheme over kNN to correct the classification bias.”