A Divide-and-Conquer Approach to Geometric Sampling for Active Learning
This work addresses the challenge of active learning performance degradation due to insufficient initial labels, offering a domain-specific solution for machine learning applications.
The paper tackles the problem of active learning's reliance on uncertainty evaluation by proposing a geometric sampling approach based on cluster boundary points, resulting in a method that significantly outperforms state-of-the-art baselines in cluster boundary detection and classification tasks.
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.