# Nonparametric active learning for cost-sensitive classification

arXiv: https://arxiv.org/abs/2310.00511 · HTML: https://mlsanity.com/paper/2310.00511/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 30 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [Minimax Analysis of Active Learning](https://mlsanity.com/paper/1410.0996/) (2014)
- [Active Learning for Cost-Sensitive Classification](https://mlsanity.com/paper/1703.01014/) (2017)
- [Adaptivity to Noise Parameters in Nonparametric Active Learning](https://mlsanity.com/paper/1703.05841/) (2017)
- [Nuances in Margin Conditions Determine Gains in Active Learning](https://mlsanity.com/paper/2110.08418/) (2021)
- [Minimax rates for cost-sensitive learning on manifolds with approximate nearest neighbours](https://mlsanity.com/paper/1803.00310/) (2018)

## Abstract (arXiv)

Cost-sensitive learning is a common type of machine learning problem where different errors of prediction incur different costs. In this paper, we design a generic nonparametric active learning algorithm for cost-sensitive classification. Based on the construction of confidence bounds for the expected prediction cost functions of each label, our algorithm sequentially selects the most informative vector points. Then it interacts with them by only querying the costs of prediction that could be the smallest. We prove that our algorithm attains optimal rate of convergence in terms of the number of interactions with the feature vector space. Furthermore, in terms of a general version of Tsybakov's noise assumption, the gain over the corresponding passive learning is explicitly characterized by the probability-mass of the boundary decision. Additionally, we prove the near-optimality of obtained upper bounds by providing matching (up to logarithmic factor) lower bounds.