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.
翻译:颗粒球计算是一种高效、稳健和可扩缩的颗粒球计算方法。 颗粒球计算的基础是颗粒球生成法。 本文建议了一种方法, 利用分区来加速颗粒球生成, 以取代$k$- 平均值。 它可以大大提高颗粒球生成的效率, 同时确保与现有方法相似的准确性。 此外, 还在考虑消除颗粒球的重叠和其他一些因素, 从而提出了一种新的颗粒球生成方法。 这使得颗粒球生成过程在真正意义上成为无参数和完全适应性的过程。 此外, 本文首先提供了颗粒球覆盖的数学模型。 一些真实数据集的实验结果表明, 拟议的两种颗粒球生成方法在适应性或加速性实现的同时与现有方法相似。