Quantifying With Only Positive Training Data

TL;DR

This paper introduces a new setting called One-class Quantification (OCQ) and proposes the Passive Aggressive Threshold (PAT) method, unifying PUL and OCQ, demonstrating that PAT is fast and accurate, with ExTIcE improving negative sample quantification.

Contribution

The paper unifies PUL and OCQ under a common framework and introduces PAT and ExTIcE algorithms, advancing quantification methods for scenarios with limited class information.

Findings

PAT is generally the fastest and most accurate algorithm.

ExTIcE outperforms other methods in scenarios with identical positive and negative observations.

PAT models can be reused for different data samples.

Abstract

Quantification is the research field that studies methods for counting the number of data points that belong to each class in an unlabeled sample. Traditionally, researchers in this field assume the availability of labelled observations for all classes to induce a quantification model. However, we often face situations where the number of classes is large or even unknown, or we have reliable data for a single class. When inducing a multi-class quantifier is infeasible, we are often concerned with estimates for a specific class of interest. In this context, we have proposed a novel setting known as One-class Quantification (OCQ). In contrast, Positive and Unlabeled Learning (PUL), another branch of Machine Learning, has offered solutions to OCQ, despite quantification not being the focal point of PUL. This article closes the gap between PUL and OCQ and brings both areas together under a unified view. We compare our method, Passive Aggressive Threshold (PAT), against PUL methods and show that PAT generally is the fastest and most accurate algorithm. PAT induces quantification models that can be reused to quantify different samples of data. We additionally introduce Exhaustive TIcE (ExTIcE), an improved version of the PUL algorithm Tree Induction for c Estimation (TIcE). We show that ExTIcE quantifies more accurately than PAT and the other assessed algorithms in scenarios where several negative observations are identical to the positive ones.