Approximate Borderline Sampling using Granular-Ball for Classification Tasks

TL;DR

This paper introduces a novel granular-ball based sampling method with borderline sampling capabilities, improving classifier robustness and noise handling in classification tasks.

Contribution

It proposes a restricted diffusion-based granular-ball generation and a GB-based approximate borderline sampling method, addressing boundary blurring and noise issues.

Findings

Outperforms existing GB-based sampling methods.

Effective in noisy datasets without needing purity thresholds.

Enhances classifier robustness and data quality.

Abstract

Data sampling enhances classifier efficiency and robustness through data compression and quality improvement. Recently, the sampling method based on granular-ball (GB) has shown promising performance in generality and noisy classification tasks. However, some limitations remain, including the absence of borderline sampling strategies and issues with class boundary blurring or shrinking due to overlap between GBs. In this paper, an approximate borderline sampling method using GBs is proposed for classification tasks. First, a restricted diffusion-based GB generation (RD-GBG) method is proposed, which prevents GB overlaps by constrained expansion, preserving precise geometric representation of GBs via redefined ones. Second, based on the concept of heterogeneous nearest neighbor, a GB-based approximate borderline sampling (GBABS) method is proposed, which is the first general sampling method capable of both borderline sampling and improving the quality of class noise datasets. Additionally, since RD-GBG incorporates noise detection and GBABS focuses on borderline samples, GBABS performs outstandingly on class noise datasets without the need for an optimal purity threshold. Experimental results demonstrate that the proposed methods outperform the GB-based sampling method and several representative sampling methods. Our source code is publicly available at https://github.com/CherylTse/GBABS.