Multigranulation Super-Trust Model for Attribute Reduction

IEEE As big data often contains a significant amount of uncertain, unstructured and imprecise data that are structurally complex and incomplete, traditional attribute reduction methods are less effective when applied to large-scale incomplete information systems to extract knowledge. Multigranular computing provides a powerful tool for use in big data analysis conducted at different levels of information granularity. In this paper, we present a novel multigranulation super-trust fuzzy-rough set-based attribute reduction (MSFAR) algorithm to support the formation of hierarchies of information granules of higher types and higher orders, which addresses newly emerging data mining problems in big data analysis. First, a multigranulation super-trust model based on the valued tolerance relation is constructed to identify the fuzzy similarity of the changing knowledge granularity with multimodality attributes. Second, an ensemble consensus compensatory scheme is adopted to calculate the multigranular trust degree based on the reputation at different granularities to create reasonable subproblems with different granulation levels. Third, an equilibrium method of multigranular-coevolution is employed to ensure a wide range of balancing of exploration and exploitation and can classify super elitists’ preferences and detect noncooperative behaviors with a global convergence ability and high search accuracy. The experimental results demonstrate that the MSFAR algorithm achieves a high performance in addressing uncertain and fuzzy attribute reduction problems with a large number of multigranularity variables

变精度多粒度粗糙集近似集更新的矩阵算法

随着信息大爆炸时代的到来,数据集的巨大化、数据集结构的复杂化已经成为近似计算中一个不能忽视的问题。动态计算是解决这些问题的一种行之有效的途径。本文对现有的应用于经典多粒度粗糙集动态近似集更新方法进行了改进,提出了应用于变精度多粒度粗糙集的向量矩阵近似计算与更新方法。首先,提出了一种基于向量矩阵的变精度多粒度粗糙集的近似集静态计算算法；其次,重新考虑了变精度多粒度粗糙集近似集更新时的搜索区域,并根据变精度多粒度粗糙集的性质缩小了该区域,这能有效地提升近似集更新算法的时间效率；再次,根据新的搜索区域,在变精度多粒度粗糙集近似集静态计算算法的基础上,提出了一种新的变精度多粒度粗糙集近似集更新的向量矩阵算法；最后,进行实验验证了本文提出的算法的有效性

变精度多粒度粗糙集近似集更新的矩阵算法

随着信息大爆炸时代的到来,数据集的巨大化和数据集结构的复杂化已经成为近似计算中不能忽视的问题,而动态计算是解决这些问题的一种行之有效的途径。对现有的应用于经典多粒度粗糙集动态近似集更新方法进行了改进,提出了应用于变精度多粒度粗糙集(VPMGRS)的向量矩阵近似集计算与更新方法。首先,提出了一种基于向量矩阵的VPMGRS近似集静态计算算法;其次,重新考虑了VPMGRS近似集更新时的搜索区域,并根据VPMGRS的性质缩小了该区域,有效地提升了近似集更新算法的时间效率;再次,根据新的搜索区域,在VPMGRS近似集静态计算算法的基础上提出了一种新的VPMGRS近似集更新的向量矩阵算法;最后,通过实验验证了所提算法的有效性。国家自然科学基金资助项目(11871259,61379021);国家自然科学基金青年项目(11701258)~

Knowledge reduction of dynamic covering decision information systems with varying attribute values

Knowledge reduction of dynamic covering information systems involves with the time in practical situations. In this paper, we provide incremental approaches to computing the type-1 and type-2 characteristic matrices of dynamic coverings because of varying attribute values. Then we present incremental algorithms of constructing the second and sixth approximations of sets by using characteristic matrices. We employ experimental results to illustrate that the incremental approaches are effective to calculate approximations of sets in dynamic covering information systems. Finally, we perform knowledge reduction of dynamic covering information systems with the incremental approaches