Multi-class Active Learning: A Hybrid Informative and Representative Criterion Inspired Approach

TL;DR

This paper introduces a multi-class active learning method that combines informativeness and representativeness into a unified criterion, improving label efficiency and classification performance on multi-class datasets.

Contribution

It proposes a hybrid criterion for multi-class active learning that unifies informativeness and representativeness, addressing limitations of binary-focused methods.

Findings

Outperforms state-of-the-art active learning methods on UCI datasets.

Effectively balances informativeness and representativeness in multi-class scenarios.

Reduces labeling effort while maintaining high classification accuracy.

Abstract

Labeling each instance in a large dataset is extremely labor- and time- consuming . One way to alleviate this problem is active learning, which aims to which discover the most valuable instances for labeling to construct a powerful classifier. Considering both informativeness and representativeness provides a promising way to design a practical active learning. However, most existing active learning methods select instances favoring either informativeness or representativeness. Meanwhile, many are designed based on the binary class, so that they may present suboptimal solutions on the datasets with multiple classes. In this paper, a hybrid informative and representative criterion based multi-class active learning approach is proposed. We combine the informative informativeness and representativeness into one formula, which can be solved under a unified framework. The informativeness is measured by the margin minimum while the representative information is measured by the maximum mean discrepancy. By minimizing the upper bound for the true risk, we generalize the empirical risk minimization principle to the active learning setting. Simultaneously, our proposed method makes full use of the label information, and the proposed active learning is designed based on multiple classes. So the proposed method is not suitable to the binary class but also the multiple classes. We conduct our experiments on twelve benchmark UCI data sets, and the experimental results demonstrate that the proposed method performs better than some state-of-the-art methods.