An Efficient and Adaptive Granular-ball Generation Method in Classification Problem

TL;DR

This paper introduces a faster, adaptive granular-ball generation method for classification that maintains accuracy, eliminating the need for parameters and improving efficiency through a division-based approach instead of k-means.

Contribution

It proposes a novel division-based acceleration method and an adaptive, parameter-free granular-ball generation technique with mathematical modeling for classification tasks.

Findings

Similar accuracy to existing methods

Significant improvement in generation efficiency

Fully adaptive and parameter-free process

Abstract

Granular-ball computing is an efficient, robust, and scalable learning method for granular computing. The basis of granular-ball computing is the granular-ball generation method. This paper proposes a method for accelerating the granular-ball generation using the division to replace $k$-means. It can greatly improve the efficiency of granular-ball generation while ensuring the accuracy similar to the existing method. Besides, a new adaptive method for the granular-ball generation is proposed by considering granular-ball's overlap eliminating and some other factors. This makes the granular-ball generation process of parameter-free and completely adaptive in the true sense. In addition, this paper first provides the mathematical models for the granular-ball covering. The experimental results on some real data sets demonstrate that the proposed two granular-ball generation methods have similar accuracies with the existing method while adaptiveness or acceleration is realized.