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

• URL: http://arxiv.org/abs/2201.04343v1
• Date: Wed, 12 Jan 2022 07:26:19 GMT
• Title: An Efficient and Adaptive Granular-ball Generation Method in Classification Problem
• Authors: Shuyin Xia, Xiaochuan Dai, Guoyin Wang, Xinbo Gao, Elisabeth Giem
• Abstract summary: Granular-ball computing is an efficient, robust, and scalable learning method for granular computing. 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.
• Score: 69.02474089703678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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