Active learning from positive and unlabeled examples

TL;DR

This paper introduces a theoretical analysis of active positive-unlabeled learning, where the learner adaptively queries unlabeled data with probabilistic label revelation, relevant for applications like advertising and anomaly detection.

Contribution

It provides the first theoretical analysis of label complexity in active PU learning, considering probabilistic label revelation upon querying.

Findings

First theoretical analysis of label complexity in active PU learning

Quantifies the number of queries needed for effective learning

Applicable to real-world scenarios like advertising and anomaly detection

Abstract

Learning from positive and unlabeled data (PU learning) is a weakly supervised variant of binary classification in which the learner receives labels only for (some) positively labeled instances, while all other examples remain unlabeled. Motivated by applications such as advertising and anomaly detection, we study an active PU learning setting where the learner can adaptively query instances from an unlabeled pool, but a queried label is revealed only when the instance is positive and an independent coin flip succeeds; otherwise the learner receives no information. In this paper, we provide the first theoretical analysis of the label complexity of active PU learning.