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A random forests quantile classifier for class imbalanced data
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DOI：10.1016/j.patcog.2019.01.036.png)
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摘要
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Extending previous work on quantile classifiers (q-classifiers) we propose the q*-classifier for the class imbalance problem. The classifier assigns a sample to the minority class if the minority class conditional probability exceeds 0 < q* < 1, where g* equals the unconditional probability of observing a minority class sample. The motivation for q*-classification stems from a density-based approach and leads to the useful property that the q*-classifier maximizes the sum of the true positive and true negative rates. Moreover, because the procedure can be equivalently expressed as a cost-weighted Bayes classifier, it also minimizes weighted risk. Because of this dual optimization, the q*-classifier can achieve near zero risk in imbalance problems, while simultaneously optimizing true positive and true negative rates. We use random forests to apply q*-classification. This new method which we call RFQ is shown to outperform or is competitive with existing techniques with respect to G-mean performance and variable selection. Extensions to the multiclass imbalanced setting are also considered. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Weighted Bayes classifier
Response-based sampling
Class imbalance
Minority class
Random forests
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IF:
7.6
论文数:
1.3W
被引数:
4.5W
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