Optimal Scale Combination Selection Integrating Three-Way Decision With Hasse Diagram

Knowledge-Level Fusion: A Novel Information Fusion Mode From the Perspective of Granular Computing

In recent years, with the rapid development of the Internet, multisource information fusion has become a forefront issue due to its ability to merge different information. Granular computing (GrC), as a methodology simulating human hierarchical cognition, provides a new approach for multisource information fusion. However, on one hand, the existing information fusion studies in GrC all focus on feature-level fusion and decision-level fusion based on multisource data, neglecting the basic characteristics and advantages of GrC: granulation. On the other hand, the existing methods for fusing the knowledge spaces in GrC suffer from losing the necessary information or artificially adding information. In order to address these issues, a novel information fusion mode from the perspective of GrC is proposed in this article, named knowledge-level fusion. First, by introducing a new step, that is, granulate data to construct the knowledge space, into the multisource information fusion process, the knowledge-level fusion mode is proposed. Second, the optimistic core quotient space is proposed to characterize the information consensus and information gap of multisource knowledge spaces in the static data environment. The pessimistic core quotient space is proposed to characterize the information consensus in the dynamic data environment. Related theorems are given to describe the characteristics of the core quotient spaces. Then, the knowledge-level fusion method driven jointly by the data space and the knowledge space is introduced based on the principle of extracting the core quotient space first and then allocating other objects in the candidate set. On the basis, the superiority of the proposed method over the existing methods is demonstrated through theoretical analysis. Finally, experiments on 12 UCI datasets and three UKB datasets are carried out to verify the promoting effect on classification and clustering algorithms, the effectiveness compared to feature-level and decision-level fusion modes, efficiency and statistical significance of the proposed knowledge-level fusion method and mode.

Multigranularity Data Analysis With Zentropy Uncertainty Measure for Efficient and Robust Feature Selection

Multigranularity data analysis has recently become an active research topic in the intelligent computing and data mining fields. Feature selection via multigranularity data analysis is an effective tool for characterizing hierarchical data and enhancing the accuracy of the results. Although the multigranularity data analysis method has been widely adopted for feature selection, existing studies still present one prevalent disadvantage: multigranularity data analysis mostly focuses on information presented at a single granularity while ignoring the hierarchical structure of multigranularity data, which is contrary to the nature of multigranularity. Hence, this article proposes a multigranularity data analysis with a zentropy uncertainty measure for efficient and robust feature selection. Specifically, a consistent degree is first introduced to obtain optimal granularity combinations and establish an efficient neighborhood model for multigranularity information processing. Then, a novel and robust uncertainty measure is developed by integrating the multigranularity information, namely the zentropy-based measure. Considering its accuracy among uncertainty measures, two important measures are further designed and applied to feature selection. Extensive experiments demonstrate that the proposed method can achieve better robustness and classification performance than other state-of-the-art methods.

Effective Value Analysis of Fuzzy Similarity Relation in HQSS for Efficient Granulation

Hierarchical quotient space structure (HQSS), as a typical description of granular computing (GrC), focuses on hierarchically granulating fuzzy data and mining hidden knowledge. The key step of constructing HQSS is to transform the fuzzy similarity relation into fuzzy equivalence relation. However, on one hand, the transformation process has high time complexity. On the other hand, it is difficult to mine knowledge directly from fuzzy similarity relation due to its information redundancy, i.e., sparsity of effective information. Therefore, this article mainly focuses on proposing an efficient granulation approach for constructing HQSS by quickly extracting the effective value of fuzzy similarity relation. First, the effective value and effective position of fuzzy similarity relation are defined according to whether they could be retained in fuzzy equivalence relation. Second, the number and composition of effective values are presented to confirm that which elements are effective values. Based on these above theories, redundant information and sparse effective information in fuzzy similarity relation could be completely distinguished. Next, both isomorphism and similarity between two fuzzy similarity relations are researched based on the effective value. The isomorphism between two fuzzy equivalence relations is discussed based on the effective value. Then, the algorithm with low time complexity for extracting effective values of fuzzy similarity relation is introduced. On the basis, the algorithm for constructing HQSS is presented to realize efficient granulation of fuzzy data. The proposed algorithms could accurately extract effective information from the fuzzy similarity relation and construct the same HQSS with the fuzzy equivalence relation while greatly reducing the time complexity. Finally, relevant experiments on 15 UCI datasets, 3 UKB datasets, and 5 image datasets are shown and analyzed to verify the effectiveness and efficiency of the proposed algorithm.

STIF: Intuitionistic fuzzy Gaussian membership function with statistical transformation weight of evidence and information value for private information preservation

Data sharing to the multiple organizations are essential for analysis in many situations. The shared data contains the individual's private and sensitive information and results in privacy breach. To overcome the privacy challenges, privacy preserving data mining (PPDM) has progressed as a solution. This work addresses the problem of PPDM by proposing statistical transformation with intuitionistic fuzzy (STIF) algorithm for data perturbation. The STIF algorithm contains statistical methods weight of evidence, information value and intuitionistic fuzzy Gaussian membership function. The STIF algorithm is applied on three benchmark datasets adult income, bank marketing and lung cancer. The classifier models decision tree, random forest, extreme gradient boost and support vector machines are used for accuracy and performance analysis. The results show that the STIF algorithm achieves 99% of accuracy for adult income dataset and 100% accuracy for both bank marketing and lung cancer datasets. Further, the results highlights that the STIF algorithm outperforms in data perturbation capacity and privacy preserving capacity than the state-of-art algorithms without any information loss on both numerical and categorical data.