论文标题
Pawlak粗糙集和邻里粗糙套装的统一粒状球学习模型
A Unified Granular-ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set
论文作者
论文摘要
Pawlak粗糙集和邻居粗糙集是两个最常见的粗糙集理论模型。 Pawlak可以使用等效类来表示知识，但无法处理连续数据。邻里粗糙集可以处理连续数据，但它失去了使用等价类代表知识的能力。为此，本文根据粒状球计算提出了一个颗粒球粗糙集。颗粒球粗糙的套件可以同时代表Pawlak粗糙集，而邻居粗糙集可以实现两者的统一表示。这使得粒状球粗糙集不仅可以处理连续数据，而且可以使用等效类来进行知识表示。此外，我们提出了一种粒状棒状粗糙集的实施算法。基准数据集上的实验结果表明，由于颗粒球计算的鲁棒性和适应性的结合，与Pawlak Rough set和传统的邻里粗糙集相比，颗粒球粗糙集的学习精度得到了极大的提高。颗粒球粗糙集还优于九种流行或最先进的特征选择方法。
Pawlak rough set and neighborhood rough set are the two most common rough set theoretical models. Pawlak can use equivalence classes to represent knowledge, but it cannot process continuous data; neighborhood rough sets can process continuous data, but it loses the ability of using equivalence classes to represent knowledge. To this end, this paper presents a granular-ball rough set based on the granular-ball computing. The granular-ball rough set can simultaneously represent Pawlak rough sets, and the neighborhood rough set, so as to realize the unified representation of the two. This makes the granular-ball rough set not only can deal with continuous data, but also can use equivalence classes for knowledge representation. In addition, we propose an implementation algorithms of granular-ball rough sets. The experimental results on benchmark datasets demonstrate that, due to the combination of the robustness and adaptability of the granular-ball computing, the learning accuracy of the granular-ball rough set has been greatly improved compared with the Pawlak rough set and the traditional neighborhood rough set. The granular-ball rough set also outperforms nine popular or the state-of-the-art feature selection methods.
