Chance constrained uncertain classification via robust optimization
| Autor: | Sahely Bhadra, Chiranjib Bhattacharyya, J. Saketha Nath, Aharon Ben-Tal |
|---|---|
| Přispěvatelé: | Econometrics and Operations Research |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Mathematical optimization
Uncertain data Maximum-Margin Classification General Mathematics Probabilistic logic Bernstein inequalities Bernstein Inequalities Robust optimization Chance-Constraints Socp Bernstein polynomial Bounding overwatch Convex optimization Algorithm Software Computer Science & Automation Test data Mathematics |
| Zdroj: | Mathematical Programming, 127(1), 145-173. Springer |
| ISSN: | 2381-3652 0025-5610 |
| Popis: | This paper studies the problem of constructing robust classifiers when the training is plagued with uncertainty. The problem is posed as a Chance-Constrained Program (CCP) which ensures that the uncertain data points are classified correctly with high probability. Unfortunately such a CCP turns out to be intractable. The key novelty is in employing Bernstein bounding schemes to relax the CCP as a convex second order cone program whose solution is guaranteed to satisfy the probabilistic constraint. Prior to this work, only the Chebyshev based relaxations were exploited in learning algorithms. Bernstein bounds employ richer partial information and hence can be far less conservative than Chebyshev bounds. Due to this efficient modeling of uncertainty, the resulting classifiers achieve higher classification margins and hence better generalization. Methodologies for classifying uncertain test data points and error measures for evaluating classifiers robust to uncertain data are discussed. Experimental results on synthetic and real-world datasets show that the proposed classifiers are better equipped to handle data uncertainty and outperform state-of-the-art in many cases. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink