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.
@article{arxiv.2201.04343,
title = {An Efficient and Adaptive Granular-ball Generation Method in Classification Problem},
author = {Shuyin Xia and Xiaochuan Dai and Guoyin Wang and Xinbo Gao and Elisabeth Giem},
journal= {arXiv preprint arXiv:2201.04343},
year = {2022}
}