Resampling, Relabeling, & Raking Algorithm to One-Class Classification

The performance of a classification model significantly depends on the degree to which the support of each data class overlaps. Successfully distinguishing classes is difficult if the support is similar while classes differ. In the one-class classification (OCC) problem, wherein the data comprise only a single class, classifier performance is significantly degraded if the population support of each class is similar. In this study, we propose a preprocessing algorithm that enhances classifier performance by utilizing macro information that is most easily obtainable in these two problem situations. The algorithm aims to improve classifier performance by reprocessing the given data into data with mitigated class imbalance through raking and sampling techniques. This improvement in performance is demonstrated by comparing representative classifiers used in the existing OCC problem with traditional binary classifier models, unavailable on the single-class dataset.