论文标题
分类问题中有效且适应性的粒状球生成方法
An Efficient and Adaptive Granular-ball Generation Method in Classification Problem
论文作者
论文摘要
粒状球计算是一种用于颗粒计算的有效，健壮且可扩展的学习方法。粒状球计算的基础是颗粒球生成方法。本文提出了一种使用该部门加速粒状球生成的方法，以替换$ k $ -MEANS。它可以大大提高粒状球产生的效率，同时确保与现有方法相似的准确性。此外，通过考虑颗粒球消除的重叠和其他一些因素，提出了一种用于颗粒球产生的新型自适应方法。这使得无参数的颗粒球生成过程在真正的意义上是完全自适应的。此外，本文首先为颗粒球覆盖率提供了数学模型。一些实际数据集的实验结果表明，所提出的两种颗粒球生成方法与现有方法具有相似的精度，同时实现了适应性或加速度。
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.
