Conformal Risk Control for Ordinal Classification

TL;DR

This paper extends conformal prediction to ordinal classification, proposing risk control methods with theoretical guarantees and tailored loss functions, validated on multiple datasets.

Contribution

It formulates ordinal classification within conformal risk control, introduces two loss functions, and develops algorithms with theoretical risk bounds.

Findings

Effective risk control demonstrated on diverse datasets

Two tailored loss functions for ordinal tasks

Theoretical risk bounds established

Abstract

As a natural extension to the standard conformal prediction method, several conformal risk control methods have been recently developed and applied to various learning problems. In this work, we seek to control the conformal risk in expectation for ordinal classification tasks, which have broad applications to many real problems. For this purpose, we firstly formulated the ordinal classification task in the conformal risk control framework, and provided theoretic risk bounds of the risk control method. Then we proposed two types of loss functions specially designed for ordinal classification tasks, and developed corresponding algorithms to determine the prediction set for each case to control their risks at a desired level. We demonstrated the effectiveness of our proposed methods, and analyzed the difference between the two types of risks on three different datasets, including a simulated dataset, the UTKFace dataset and the diabetic retinopathy detection dataset.