# On Reject and Refine Options in Multicategory Classification

## TL;DR

This paper introduces margin-based multicategory classification methods with reject and refine options, improving error identification and providing more informative predictions, supported by theoretical analysis and empirical validation.

## Contribution

It proposes novel reject and refine options for multicategory classification, with theoretical guarantees and efficient algorithms, extending binary reject options to more complex settings.

## Key findings

- Effective identification of error-prone observations.
- Refine option improves classification by ruling out unlikely classes.
- Methods outperform standard multicategory classifiers in simulations and real data.

## Abstract

In many real applications of statistical learning, a decision made from misclassification can be too costly to afford; in this case, a reject option, which defers the decision until further investigation is conducted, is often preferred. In recent years, there has been much development for binary classification with a reject option. Yet, little progress has been made for the multicategory case. In this article, we propose margin-based multicategory classification methods with a reject option. In addition, and more importantly, we introduce a new and unique refine option for the multicategory problem, where the class of an observation is predicted to be from a set of class labels, whose cardinality is not necessarily one. The main advantage of both options lies in their capacity of identifying error-prone observations. Moreover, the refine option can provide more constructive information for classification by effectively ruling out implausible classes. Efficient implementations have been developed for the proposed methods. On the theoretical side, we offer a novel statistical learning theory and show a fast convergence rate of the excess $\ell$-risk of our methods with emphasis on diverging dimensionality and number of classes. The results can be further improved under a low noise assumption. A set of comprehensive simulation and real data studies has shown the usefulness of the new learning tools compared to regular multicategory classifiers. Detailed proofs of theorems and extended numerical results are included in the supplemental materials available online.