A Distance-Based Method for Attribute Reduction in Incomplete Decision Systems

Demetrovics, János; Thi, Vu Duc; Giang, Nguyễn Long

doi:10.55630/sjc.2013.7.355-374

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…Indynamiccompletedecisionsystems,manyincrementalalgorithmsforfindingreducthave beenproposedbasedontraditionalroughsetandsomeextendedroughsetsofar.Whenaddingor deleting objects, researchers proposed many incremental attribute reduction algorithms based on different measures such as distance measure (Demetrovics et al, 2014;, informationgranularity (Jing,Li,Fujita,Yu,&Wang,2017;Jing,Li,Huang,Chen,&Hong,2017), fuzzyattributedependency (Liuetal.,2017),fuzzydiscernibilityrelation (Yangetal.,2017),extended discernibilitymatrix (Langetal.,2017;Yangetal.,2017;Weietal.,2018;Maetal.,2019),positive domain (Das,Sengupta&Bhattacharyya,2018;Langetal.,2018;Haoetal.,2019),membership function (Shua,Qian&Xie,2019),simplifieddiscernibilitymatrix (Yangetal.,2019),indiscernibility relation (Nandhini&Thangadurai,2019),coveringgranularity (Caietal.,2019),informationentropy (Shu,Qian&Xie,2020),keyinstancesetinfuzzyroughset(Nietal.,2020,fuzzyroughsetbased informationentropy (Zhangetal.,2020),discernibilitymatrix(Liuetal.,2020.…”

Section: Introductionmentioning

confidence: 99%

Filter-Wrapper Incremental Algorithms for Finding Reduct in Incomplete Decision Systems When Adding and Deleting an Attribute Set

Giang

Sơn

Tuấn³

et al. 2021

International Journal of Data Warehousing and Mining

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The tolerance rough set model is an effective tool to solve attribute reduction problem directly on incomplete decision systems without pre-processing missing values. In practical applications, incomplete decision systems are often changed and updated, especially in the case of adding or removing attributes. To solve the problem of finding reduct on dynamic incomplete decision systems, researchers have proposed many incremental algorithms to decrease execution time. However, the proposed incremental algorithms are mainly based on filter approach in which classification accuracy was calculated after the reduct has been obtained. As the results, these filter algorithms do not get the best result in term of the number of attributes in reduct and classification accuracy. This paper proposes two distance based filter-wrapper incremental algorithms: the algorithm IFWA_AA in case of adding attributes and the algorithm IFWA_DA in case of deleting attributes. Experimental results show that proposed filter-wrapper incremental algorithm IFWA_AA decreases significantly the number of attributes in reduct and improves classification accuracy compared to filter incremental algorithms such as UARA, IDRA.

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Section: Introductionmentioning

confidence: 99%