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Poster
Class-Weighted Classification: Trade-offs and Robust Approaches
Ziyu Xu · Chen Dan · Justin Khim · Pradeep Ravikumar

Wed Jul 15 08:00 AM -- 08:45 AM & Wed Jul 15 09:00 PM -- 09:45 PM (PDT) @ Virtual
in Poster Session 22 »

We consider imbalanced classification, the problem in which a label may have low marginal probability relative to other labels, by weighting losses according to the correct class. First, we examine the convergence rates of the expected excess weighted risk of plug-in classifiers where the weighting for the plug-in classifier and the risk may be different. This leads to irreducible errors that do not converge to the weighted Bayes risk, which motivates our consideration of robust risks. We define a robust risk that minimizes risk over a set of weightings, show excess risk bounds for this problem, and demonstrate that particular choices of the weighting set leads to a special instance of conditional value at risk (CVaR) from stochastic programming, which we call label conditional value at risk (LCVaR). Additionally, we generalize this weighting to derive a new robust risk problem that we call label heterogeneous conditional value at risk (LHCVaR). Finally, we empirically demonstrate the efficacy of LCVaR and LHCVaR on improving class conditional risks.

Keywords: Learning Theory · Non-parametric Methods · Statistical Learning Theory · Supervised Learning
[ Slides] 

Author Information

Ziyu Xu (Carnegie Mellon University)
Chen Dan (Carnegie Mellon University)
Justin Khim (Carnegie Mellon University)
Pradeep Ravikumar (Carnegie Mellon University)

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