Classification Under Partial Reject Options

TL;DR

This paper develops a Bayesian set-valued classification framework that allows for partial rejections and outlier detection, optimizing decision-making based on customizable reward functions.

Contribution

It introduces a general reward-based framework for set-valued classification, deriving optimal classifiers for hypotheses grouped into blocks with different ambiguity levels.

Findings

Optimal classifiers derived for various hypothesis partition scenarios.

Application to ornithological data with MCMC parameter estimation.

Reward function tuning via cross-validation enhances classification performance.

Abstract

We study set-valued classification for a Bayesian model where data originates from one of a finite number $N$ of possible hypotheses. Thus we consider the scenario where the size of the classified set of categories ranges from 0 to $N$. Empty sets corresponds to an outlier, size 1 represents a firm decision that singles out one hypotheses, size $N$ corresponds to a rejection to classify, whereas sizes $2\ldots,N-1$ represent a partial rejection, where some hypotheses are excluded from further analysis. We introduce a general framework of reward functions with a set-valued argument and derive the corresponding optimal Bayes classifiers, for a homogeneous block of hypotheses and for when hypotheses are partitioned into blocks, where ambiguity within and between blocks are of different severity. We illustrate classification using an ornithological dataset, with taxa partitioned into blocks and parameters estimated using MCMC. The associated reward function's tuning parameters are chosen through cross-validation.