Class-Weighted Classification: Trade-offs and Robust Approaches

By Ziyu Xu, Chen Dan, Justin Khim, Pradeep Ravikumar. 2020

56 citations recorded by the source corpus.

We address 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 and show excess risk bounds for this problem. Finally, we show 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.

Subfields: Artificial Intelligence, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Li Shen
2. Mingchen Gao
3. Zhenyi Wang
4. Qiuling Suo
5. Le Fang
6. Tiehang Duan

In Statistics and Probability

1. Le Fang
2. Li Shen
3. Zhenyi Wang
4. Tiehang Duan
5. Mingchen Gao
6. Qiuling Suo

Citations in the corpus

• Improving Task-free Continual Learning by Distributionally Robust Memory Evolution (2022, background): “There are various applications of DRO in machine learning problems, including tackling the group-shift (Sagawa et al., 2020), subpopulation shift (Zhai et al., 2021), and class imbalances (Xu et al., 2020).”
• DORO: Distributional and Outlier Robust Optimization (2021, unknown): “Several recent papers (Hashimoto et al., 2018; Oren et al., 2019; Xu et al., 2020) proposed using DRO to deal with subpopulation shift, but it was also observed that DRO was prone to overfit in practice (Sagawa et al., 2020a;b).”