Multi-label Classification under Uncertainty: A Tree-based Conformal Prediction Approach

Document Type

Conference Proceeding

Publication Date

1-1-2023

Abstract

Multi-label classification is a common challenge in various machine learning applications, where a single data instance can be associated with multiple classes simultaneously. The current paper proposes a novel tree-based method for multi-label classification using conformal prediction and multiple hypothesis testing. The proposed method employs hierarchical clustering with labelsets to develop a hierarchical tree, which is then formulated as a multiple-testing problem with a hierarchical structure. The split-conformal prediction method is used to obtain marginal conformal p-values for each tested hypothesis, and two hierarchical testing procedures are developed based on marginal conformal p-values, including a hierarchical Bonferroni procedure and its modification for controlling the family-wise error rate. The prediction sets are thus formed based on the testing outcomes of these two procedures. We establish a theoretical guarantee of valid coverage for the prediction sets through proven family-wise error rate control of those two procedures. We demonstrate the effectiveness of our method in a simulation study and two real data analysis compared to other conformal methods for multi-label classification.

Identifier

85178661724 (Scopus)

Publication Title

Proceedings of Machine Learning Research

e-ISSN

26403498

First Page

488

Last Page

512

Volume

204

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