A Divide-and-Conquer Approach to Geometric Sampling for Active Learning

TL;DR

This paper introduces a geometric sampling method for active learning that bypasses traditional uncertainty evaluation, using clustering and a knight's tour strategy to improve classification with fewer labels.

Contribution

It proposes a novel divide-and-conquer geometric sampling approach for active learning, eliminating dependence on uncertainty evaluation and enhancing performance.

Findings

GAL significantly outperforms state-of-the-art baselines in experiments.

The method effectively detects cluster boundaries and improves classification accuracy.

The approach reduces labeling effort while maintaining high performance.

Abstract

Active learning (AL) repeatedly trains the classifier with the minimum labeling budget to improve the current classification model. The training process is usually supervised by an uncertainty evaluation strategy. However, the uncertainty evaluation always suffers from performance degeneration when the initial labeled set has insufficient labels. To completely eliminate the dependence on the uncertainty evaluation sampling in AL, this paper proposes a divide-and-conquer idea that directly transfers the AL sampling as the geometric sampling over the clusters. By dividing the points of the clusters into cluster boundary and core points, we theoretically discuss their margin distance and {hypothesis relationship}. With the advantages of cluster boundary points in the above two properties, we propose a Geometric Active Learning (GAL) algorithm by knight's tour. Experimental studies of the two reported experimental tasks including cluster boundary detection and AL classification show that the proposed GAL method significantly outperforms the state-of-the-art baselines.