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Neyman-Pearson Multi-class Classification via Cost-sensitive Learning
April 30, 2024, 4:44 a.m. | Ye Tian, Yang Feng
cs.LG updates on arXiv.org arxiv.org
Abstract: Most existing classification methods aim to minimize the overall misclassification error rate. However, in applications such as loan default prediction, different types of errors can have varying consequences. To address this asymmetry issue, two popular paradigms have been developed: the Neyman-Pearson (NP) paradigm and the cost-sensitive (CS) paradigm. Previous studies on the NP paradigm have primarily focused on the binary case, while the multi-class NP problem poses a greater challenge due to its unknown feasibility. …
abstract aim applications arxiv class classification consequences cost cs.lg error errors however issue paradigm pearson popular prediction rate stat.me stat.ml type types via
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