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Multilabel Feature Selection Based on Relative Discernibility Pair Matrix
IEEE Transactions on Fuzzy Systems, 2022In multi-label learning, the curse of dimensionality is one of major challenges. Existing single-label feature selection methods cannot be directly applied to multi-label data, and multi-label feature selections have thus been widely studied. As an effective granular computing tool, rough set theory has been applied to multi-label feature selections ...
Erliang Yao +3 more
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New Method of Discernibility Matrix Formation
Advanced Materials Research, 2013Aiming at the defect of the discernibility matrix formation, the three-process space-time function is analyzed, and the method is put forward which corresponding new elements dont involve the formation of discernibility matrix or the existed element is deleted, that is the method of discernibility matrix minimum formation. The algorithm of formation is
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Discernibility Matrix Enriching and Boolean-And Algorithm for Attributes Reduction
2014discernibility matrix and binary discernibility matrix method is easy to understand and design, which has aroused great concern by many scholar. Research shows that the two methods produce a large number of repeated and useless elements (if A is the subset of B, B is the useless element of A) on the fly. These repeated and useless elements occupy a lot
ZhangYan Xu +3 more
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Attribute reduction based on improved discernibility matrix
2010 2nd IEEE International Conference on Information Management and Engineering, 2010The attribute reduction based on information entropy is different to that based on positive region in inconsistent information system. The problem of discernibility matrix in algebra view is analyzed, and an new discernibility matrix based on information entropy is proposed in this paper.
Zhou Peng, Li Zhishu, Huang Zhiguo
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The method of attribute reduction based on discernibility matrix
2010 International Conference on Computer Application and System Modeling (ICCASM 2010), 2010With respect to decision table, especially the inconsistent ones. On the basis of equivalent partitions, This paper quantifies the elements in the same class deduced by condition attribute belonging to different decision attribute classes, generates the discernibility matrix and measures the indexes of attribute significance reasonable, and then uses ...
null Lv Yue-jin +2 more
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Attribute Reduction in Concept Lattice Based on Discernibility Matrix
2005As an effective tool for knowledge discovery, concept lattice has been successfully applied to various fields. One of the key problems of knowledge discovery is knowledge reduction. This paper studies attribute reduction in concept lattice. Using the idea similar to Skowron and Rauszer's discernibility matrix, the discernibility matrix and function of ...
Wen-Xiu Zhang, Ling Wei, Jian-Jun Qi
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A Simplified Discernibility Matrix of the Attribute Reduction Method
2010 3rd International Conference on Information Management, Innovation Management and Industrial Engineering, 2010According to existing have defects discernibility matrix, and the attribute reduction algorithm for attribute reduction algorithm of complex process. This paper made part of optimization, based on the condition attributes classify the grouping generated representative data to simplify the discernibility matrix, and the order of the discernibility ...
Weilai Hao, Xuebin Zhang
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Belief Discernibility Matrix and Function for Incremental or Large Data
2013This paper proposes an incremental attribute selection method based on rough sets from partially uncertain and incremental or large decision system. The uncertainty exists only in the decision attributes classes and is represented by the belief function theory.
Salsabil Trabelsi +2 more
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Heuristic for Attribute Selection Using Belief Discernibility Matrix
2012This paper proposes a new heuristic attribute selection method based on rough sets to remove the superfluous attributes from partially uncertain data. We handle uncertainty only in decision attributes (classes) under the belief function framework.
Salsabil Trabelsi +2 more
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Dimensionality reduction of EEG signal using Fuzzy Discernibility Matrix
2017 10th International Conference on Human System Interactions (HSI), 2017High dimensionality of feature space is a problem in supervised machine learning. Redundant or superfluous features either slow down the training process or dilute the quality of classification. Many methods are available in literature for dimensionality reduction.
Rajdeep Chatterjee +3 more
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