Coevolutionary fuzzy attribute order reduction with complete attribute-value space tree

Since big data sets are structurally complex, high-dimensional, and their attributes exhibit some redundant and irrelevant information, the selection, evaluation, and combination of those large-scale attributes pose huge challenges to traditional methods. Fuzzy rough sets have emerged as a powerful vehicle to deal with uncertain and fuzzy attributes in big data problems that involve a very large number of variables to be analyzed in a very short time. In order to further overcome the inefficiency of traditional algorithms in the uncertain and fuzzy big data, in this paper we present a new coevolutionary fuzzy attribute order reduction algorithm (CFAOR) based on a complete attribute-value space tree. A complete attribute-value space tree model of decision table is designed in the attribute space to adaptively prune and optimize the attribute order tree. The fuzzy similarity of multimodality attributes can be extracted to satisfy the needs of users with the better convergence speed and classification performance. Then, the decision rule sets generate a series of rule chains to form an efficient cascade attribute order reduction and classification with a rough entropy threshold. Finally, the performance of CFAOR is assessed with a set of benchmark problems that contain complex high dimensional datasets with noise. The experimental results demonstrate that CFAOR can achieve the higher average computational efficiency and classification accuracy, compared with the state-of-the-art methods. Furthermore, CFAOR is applied to extract different tissues surfaces of dynamical changing infant cerebral cortex and it achieves a satisfying consistency with those of medical experts, which shows its potential significance for the disorder prediction of infant cerebrum

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

A novel granular approach for detecting dynamic online communities in social network

The great surge in the research of community discovery in complex network is going on due to its challenging aspects. Dynamicity and overlapping nature are among the common characteristics of these networks which are the main focus of this paper. In this research, we attempt to approximate the granular human-inspired viewpoints of the networks. This is especially helpful when making decisions with partial knowledge. In line with the principle of granular computing, in which precision is avoided, we define the micro- and macrogranules in two levels of nodes and communities, respectively. The proposed algorithm takes microgranules as input and outputs meaningful communities in rough macrocommunity form. For this purpose, the microgranules are drawn toward each other based on a new rough similarity measure defined in this paper. As a result, the structure of communities is revealed and adapted over time, according to the interactions observed in the network, and the number of communities is extracted automatically. The proposed model can deal with both the low and the sharp changes in the network. The algorithm is evaluated in multiple dynamic datasets and the results confirm the superiority of the proposed algorithm in various measures and scenarios