Active Learning for One-Class Classification Using Two One-Class Classifiers

TL;DR

This paper presents a new active learning approach for one-class classification using two classifiers, improving query strategies and stopping criteria, and demonstrating superior performance across multiple datasets.

Contribution

Introduces a generic active learning method for one-class classification based on two classifiers, enabling new query strategies and better performance.

Findings

Improved query strategies outperform existing methods.

Effective stopping criteria are developed for the active learning process.

Experimental results show consistent performance gains across datasets.

Abstract

This paper introduces a novel, generic active learning method for one-class classification. Active learning methods play an important role to reduce the efforts of manual labeling in the field of machine learning. Although many active learning approaches have been proposed during the last years, most of them are restricted on binary or multi-class problems. One-class classifiers use samples from only one class, the so-called target class, during training and hence require special active learning strategies. The few strategies proposed for one-class classification either suffer from their limitation on specific one-class classifiers or their performance depends on particular assumptions about datasets like imbalance. Our proposed method bases on using two one-class classifiers, one for the desired target class and one for the so-called outlier class. It allows to invent new query strategies, to use binary query strategies and to define simple stopping criteria. Based on the new method, two query strategies are proposed. The provided experiments compare the proposed approach with known strategies on various datasets and show improved results in almost all situations.