Exponentiated Gradient Exploration for Active Learning

TL;DR

This paper introduces EG-Active, a sequential algorithm that enhances active learning by combining boundary refinement with optimal random exploration, leading to improved model performance.

Contribution

The paper proposes a novel exploration method for active learning that can be integrated with existing algorithms to improve their effectiveness.

Findings

EG-Active significantly outperforms existing active learning methods.

The approach achieves statistically significant improvements in model performance.

Experimental results validate the effectiveness of the exploration strategy.

Abstract

Active learning strategies respond to the costly labelling task in a supervised classification by selecting the most useful unlabelled examples in training a predictive model. Many conventional active learning algorithms focus on refining the decision boundary, rather than exploring new regions that can be more informative. In this setting, we propose a sequential algorithm named EG-Active that can improve any Active learning algorithm by an optimal random exploration. Experimental results show a statistically significant and appreciable improvement in the performance of our new approach over the existing active feedback methods.