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arXiv · 2201.04343

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

Shuyin Xia·Xiaochuan Dai·Guoyin Wang·Xinbo Gao·Elisabeth Giem
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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.

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Shuyin Xia, Xiaochuan Dai, Guoyin Wang, Xinbo Gao, Elisabeth Giem. 2022-01-12. An Efficient and Adaptive Granular-ball Generation Method in Classification Problem. https://arxiv.org/abs/2201.04343

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