Pool-Based Active Learning with Proper Topological Regions

TL;DR

This paper introduces a novel pool-based active learning approach leveraging Proper Topological Regions (PTR) from topological data analysis to improve sample selection in multi-class classification, showing competitive results on benchmarks.

Contribution

It presents a new meta-approach using PTR from TDA for active learning, enhancing sample relevance detection in multi-class tasks.

Findings

Competitive performance on benchmark datasets

Effective in cold-start and active learning phases

Utilizes topological data analysis for sample selection

Abstract

Machine learning methods usually rely on large sample size to have good performance, while it is difficult to provide labeled set in many applications. Pool-based active learning methods are there to detect, among a set of unlabeled data, the ones that are the most relevant for the training. We propose in this paper a meta-approach for pool-based active learning strategies in the context of multi-class classification tasks based on Proper Topological Regions. PTR, based on topological data analysis (TDA), are relevant regions used to sample cold-start points or within the active learning scheme. The proposed method is illustrated empirically on various benchmark datasets, being competitive to the classical methods from the literature.