An Efficient and Adaptive Granular-ball Generation Method in Classification Problem
This work addresses efficiency and adaptiveness issues in granular-ball computing for classification tasks, representing an incremental improvement over existing methods.
The paper tackles the problem of improving efficiency and adaptiveness in granular-ball generation for classification by replacing k-means with a division method and introducing an adaptive approach, achieving similar accuracy while accelerating the process and making it parameter-free.
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.