Conjunction Subspaces Test for Conformal and Selective Classification
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909351919222784 |
|---|---|
| author | He, Zengyou Li, Zerun Dong, Junjie Liu, Xinying Jiang, Mudi Hu, Lianyu |
| author_facet | He, Zengyou Li, Zerun Dong, Junjie Liu, Xinying Jiang, Mudi Hu, Lianyu |
| contents | In this paper, we present a new classifier, which integrates significance testing results over different random subspaces to yield consensus p-values for quantifying the uncertainty of classification decision. The null hypothesis is that the test sample has no association with the target class on a randomly chosen subspace, and hence the classification problem can be formulated as a problem of testing for the conjunction of hypotheses. The proposed classifier can be easily deployed for the purpose of conformal prediction and selective classification with reject and refine options by simply thresholding the consensus p-values. The theoretical analysis on the generalization error bound of the proposed classifier is provided and empirical studies on real data sets are conducted as well to demonstrate its effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12297 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Conjunction Subspaces Test for Conformal and Selective Classification He, Zengyou Li, Zerun Dong, Junjie Liu, Xinying Jiang, Mudi Hu, Lianyu Machine Learning Artificial Intelligence In this paper, we present a new classifier, which integrates significance testing results over different random subspaces to yield consensus p-values for quantifying the uncertainty of classification decision. The null hypothesis is that the test sample has no association with the target class on a randomly chosen subspace, and hence the classification problem can be formulated as a problem of testing for the conjunction of hypotheses. The proposed classifier can be easily deployed for the purpose of conformal prediction and selective classification with reject and refine options by simply thresholding the consensus p-values. The theoretical analysis on the generalization error bound of the proposed classifier is provided and empirical studies on real data sets are conducted as well to demonstrate its effectiveness. |
| title | Conjunction Subspaces Test for Conformal and Selective Classification |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2410.12297 |