Distribution-Free Conformal Prediction for Ordinal Classification

EasyChair Preprint 13848, version 1

Abstract

Conformal prediction is a general distribution-free approach for constructing prediction sets combined with any machine learning algorithm that achieve valid marginal or conditional coverage in finite samples. Ordinal classification is common in real applications where the target variable has natural ordering among the class labels. In this paper, we discuss constructing distribution-free prediction sets for such ordinal classification problems by leveraging the ideas of conformal prediction and multiple testing with FWER control. Newer conformal prediction methods are developed for constructing contiguous and non-contiguous prediction sets based on marginal and conditional (class-specific) conformal $p$-values, respectively. Theoretically, we prove that the proposed methods respectively achieve satisfactory levels of marginal and class-specific conditional coverages. Through simulation study and real data analysis, these proposed methods show promising performance compared to the existing conformal method.

Keyphrases: FWER control, class-specific conditional coverage, conformal prediction, marginal coverage, multiple testing, ordinal classification

@booklet{EasyChair:13848,
  author    = {Subhrasish Chakraborty and Chhavi Tyagi and Haiyan Qiao and Wenge Guo},
  title     = {Distribution-Free Conformal Prediction for Ordinal Classification},
  howpublished = {EasyChair Preprint 13848},
  year      = {EasyChair, 2024}}