Knowledge Reduction in Random Incomplete Decision Tables via Evidence Theory

2012 ◽  
Vol 115 (2-3) ◽  
pp. 203-218 ◽  
Author(s):  
Wei-Zhi Wu
Keyword(s):  
Evidence Theory ◽  
Knowledge Reduction ◽  
Decision Tables

Knowledge Reduction in Inconsistent Decision Tables

2006 ◽  
pp. 626-635 ◽  
Author(s):  
Qihe Liu ◽  
Leiting Chen ◽  
Jianzhong Zhang ◽  
Fan Min
Keyword(s):  
Knowledge Reduction ◽  
Decision Tables

scholarly journals Attribute reduction in interval-valued fuzzy ordered decision tables via evidence theory

2018 ◽  
Vol 2018 (16) ◽  
pp. 1475-1482 ◽  
Author(s):  
Jia Zhang ◽  
Xiaoyan Zhang ◽  
Weihua Xu
Keyword(s):  
Attribute Reduction ◽  
Evidence Theory ◽  
Decision Tables ◽  
Interval Valued

scholarly journals Relative Reduction of Neighborhood-Covering Pessimistic Multigranulation Rough Set Based on Evidence Theory

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 334 ◽  
Author(s):  
Xiaoying You ◽  
Jinjin Li ◽  
Hongkun Wang
Keyword(s):  
Information System ◽  
Rough Set ◽  
Evidence Theory ◽  
Belief Function ◽  
Relative Reduction ◽  
Reduction Theory ◽  
Research Topics ◽  
Knowledge Reduction ◽  
Multigranulation Rough Set ◽  
Lower And Upper Approximations

Relative reduction of multiple neighborhood-covering with multigranulation rough set has been one of the hot research topics in knowledge reduction theory. In this paper, we explore the relative reduction of covering information system by combining the neighborhood-covering pessimistic multigranulation rough set with evidence theory. First, the lower and upper approximations of multigranulation rough set in neighborhood-covering information systems are introduced based on the concept of neighborhood of objects. Second, the belief and plausibility functions from evidence theory are employed to characterize the approximations of neighborhood-covering multigranulation rough set. Then the relative reduction of neighborhood-covering information system is investigated by using the belief and plausibility functions. Finally, an algorithm for computing a relative reduction of neighborhood-covering pessimistic multigranulation rough set is proposed according to the significance of coverings defined by the belief function, and its validity is examined by a practical example.

scholarly journals A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hua Li ◽  
Deyu Li ◽  
Yanhui Zhai ◽  
Suge Wang ◽  
Jing Zhang
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Mathematical Tool ◽  
Knowledge Reduction ◽  
Variable Precision ◽  
Multilabel Learning ◽  
Decision Tables

Owing to the high dimensionality of multilabel data, feature selection in multilabel learning will be necessary in order to reduce the redundant features and improve the performance of multilabel classification. Rough set theory, as a valid mathematical tool for data analysis, has been widely applied to feature selection (also called attribute reduction). In this study, we propose a variable precision attribute reduct for multilabel data based on rough set theory, calledδ-confidence reduct, which can correctly capture the uncertainty implied among labels. Furthermore, judgement theory and discernibility matrix associated withδ-confidence reduct are also introduced, from which we can obtain the approach to knowledge reduction in multilabel decision tables.

Knowledge reduction for decision tables with attribute value taxonomies

2014 ◽  
Vol 56 ◽  
pp. 68-78 ◽  
Author(s):  
Mingquan Ye ◽  
Xindong Wu ◽  
Xuegang Hu ◽  
Donghui Hu
Keyword(s):  
Knowledge Reduction ◽  
Decision Tables ◽  
Attribute Value
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