A random forests quantile classifier for class imbalanced data. (June 2019)

• Record Type: Journal Article
• Authors: O'Brien, Robert; Ishwaran, Hemant
• Is Part Of: Pattern recognition. Volume 90(2019:Jun.)
• Page Start: 232
• Page End: 249
• Publication Date: 2019-06
• Subjects: Weighted Bayes classifier -- Response-based sampling -- Class imbalance -- Minority class -- Random forests; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.01.036
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: The new classifier jointly optimizes true positive and true negative rates for imbalanced data while simultaneously minimizing weighted risk. It outperforms the existing random forests method in complex settings of rare minority instances, high dimensionality and highly imbalanced data. Its performance is superior with respect to variable selection for imbalanced data. The classifier is also highly competitive for multiclass imbalanced data. Abstract: 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 q * 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.