PUAL: A Classifier on Trifurcate Positive-Unlabeled Data

TL;DR

This paper introduces PUAL, a novel classifier designed for trifurcate positive-unlabeled data, utilizing asymmetric loss and kernel methods to improve classification performance in complex PU scenarios.

Contribution

The paper proposes a new PU classifier with asymmetric loss and kernel-based non-linear decision boundaries for trifurcate data, addressing limitations of existing methods.

Findings

PUAL achieves satisfactory classification on trifurcate data

Kernel-based PUAL captures non-linear decision boundaries

Experimental results on simulated and real datasets validate effectiveness

Abstract

Positive-unlabeled (PU) learning aims to train a classifier using the data containing only labeled-positive instances and unlabeled instances. However, existing PU learning methods are generally hard to achieve satisfactory performance on trifurcate data, where the positive instances distribute on both sides of the negative instances. To address this issue, firstly we propose a PU classifier with asymmetric loss (PUAL), by introducing a structure of asymmetric loss on positive instances into the objective function of the global and local learning classifier. Then we develop a kernel-based algorithm to enable PUAL to obtain non-linear decision boundary. We show that, through experiments on both simulated and real-world datasets, PUAL can achieve satisfactory classification on trifurcate data.