Approaches to knowledge reductions in inconsistent systems

Zhang, Wenxiu; Mi, Jin‐Xiao; Wu, Wei-Zhi

doi:10.1002/int.10128

Cited by 186 publications

(81 citation statements)

References 7 publications

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“…Ye and Chen (2002) presented a discernibility matrix for attribute reduction in consistent and inconsistent decision tables. Zhang et al (2003) studied finding reducts of inconsistent information systems. Degang et al (2007) used the discernibility matrix for finding reducts in consistent and inconsistent covering decision system.…”

Section: Attribute Reduction Methodsmentioning

confidence: 99%

Attribute reduction: a horizontal data decomposition approach

Hońko

2014

Soft Comput

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Ever-growing data generate a need for new solutions to the problem of attribute reduction. Such solutions are required to deal with limited memory capacity and with many computations needed for large data processing. This paper proposes new definitions of attribute reduction using horizontal data decomposition. Algorithms for computing reducts of an information system and decision table are developed and evaluated. In the proposed approach, the size of subtables obtained during the decomposition can be arbitrarily small. The reduct sets of subtables are computed independently from one another using any heuristic method for attribute reduction. Compared with standard attribute reduction methods, the proposed approach can produce the same reducts with less space complexity and with the same or less theoretical time complexity. Experiments conducted under this work show that for information systems with fewer attributes or reducts the time needed for computing the reduct set can be shorten.

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Section: Attribute Reduction Methodsmentioning

confidence: 99%

Attribute reduction: a horizontal data decomposition approach

Hońko

2014

Soft Comput

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“…The following Example 1 shows the progress of using the Algorithm 2.1 to get the approximations based on the grade indiscernibility relation. 6 } be the decision set. The related information system is given in Table 1.…”

Section: The Non-incremental Algorithm Of Computing Approximationsmentioning

confidence: 99%

“…It can be used in attribute value representation models to describe the dependencies among attributes, evaluate the significance of attributes and derive decision rules 2,3,4,5,6 . Rough setbased data analysis starts from a data table, also called an information system, which contains data about objects of interest that are characterized by a finite set of attributes.…”

Section: Introductionmentioning

confidence: 99%

Incremental update of rough set approximation under the grade indiscernibility relation

Luo¹,

Liu²,

Qin³

et al. 2017

IJCIS

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The incremental updating of lower and upper approximations under the variation of information systems is an important issue in rough set theory. Many incremental updating approaches with respect to different kinds of indiscernibility relations have been proposed. The grade indiscernibility relation is a fuzzification of classical Pawlak's indiscernibility relation which can characterize the similarity between objects more precisely. Based on fuzzy rough set model, this paper discusses the approaches for dynamically acquiring of the upper and lower approximations with respect to the grade indiscernibility relation when adding and removing an attribute or an object, and changing the attribute value of the object, respectively. Since the approaches are used in succession, they make the approximations can be updated correctly and effectively when any kind of possible change in the information system. Finally, extensive experiments on data sets from University of California, Irvine (UCI) show that the incremental methods effectively reduce the computing time in comparison with the traditional non-incremental method.

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“…A criterion similar to decision-monotocity has been proposed by Slezak as the majority decision criterion [31] and by Zhang et al as the maximum distribution criterion [50]. The majority decision criterion uses a binary information vector for each equivalence class to indicate to which decision class it be- Table 2 An information table longs.…”

Section: The Decision-monotocity Criterionmentioning

confidence: 99%