Minimax Risk Classifiers with 0-1 Loss
Supervised classification techniques use training samples to learn a classification rule with small expected 0 -1 loss (error probability). Conventional methods enable tractable learning and provide out-of-sample generalization by using surrogate losses instead of the 0 -1 loss and considering speci...
| Authors: | , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2023 |
| Country: | España |
| Institution: | Basque Center for Applied Mathematics (BCAM) |
| Repository: | BIRD. BCAM's Institutional Repository Data |
| OAI Identifier: | oai:bird.bcamath.org:20.500.11824/1625 |
| Online Access: | http://hdl.handle.net/20.500.11824/1625 |
| Access Level: | Open access |
| Keyword: | Supervised Classification, Robust Risk Minimization, Performance Guarantees, Generalized Maximum Entropy |
| Summary: | Supervised classification techniques use training samples to learn a classification rule with small expected 0 -1 loss (error probability). Conventional methods enable tractable learning and provide out-of-sample generalization by using surrogate losses instead of the 0 -1 loss and considering specific families of rules (hypothesis classes). This paper presents minimax risk classifiers (MRCs) that minimize the worst-case 0 -1 loss with respect to uncertainty sets of distributions that can include the underlying distribution, with a tunable confidence. We show that MRCs can provide tight performance guarantees at learning and are strongly universally consistent using feature mappings given by characteristic kernels. The paper also proposes efficient optimization techniques for MRC learning and shows that the methods presented can provide accurate classification together with tight performance guarantees in practice. |
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