Active Learning from Positive and Unlabeled Data

TL;DR

This paper introduces an active learning algorithm tailored for positive and unlabeled data, estimating class densities to select the most informative samples without needing hyper-parameters, showing promising empirical results.

Contribution

It proposes a novel active learning method specifically designed for positive and unlabeled data, addressing a gap in existing algorithms that require multi-class labels.

Findings

Effective density estimation for positive and unlabeled data

Hyper-parameter free informativeness measure

Promising empirical performance compared to similar methods

Abstract

During recent years, active learning has evolved into a popular paradigm for utilizing user's feedback to improve accuracy of learning algorithms. Active learning works by selecting the most informative sample among unlabeled data and querying the label of that point from user. Many different methods such as uncertainty sampling and minimum risk sampling have been utilized to select the most informative sample in active learning. Although many active learning algorithms have been proposed so far, most of them work with binary or multi-class classification problems and therefore can not be applied to problems in which only samples from one class as well as a set of unlabeled data are available. Such problems arise in many real-world situations and are known as the problem of learning from positive and unlabeled data. In this paper we propose an active learning algorithm that can work when only samples of one class as well as a set of unlabelled data are available. Our method works by separately estimating probability desnity of positive and unlabeled points and then computing expected value of informativeness to get rid of a hyper-parameter and have a better measure of informativeness./ Experiments and empirical analysis show promising results compared to other similar methods.