Data analysis based on discernibility and indiscernibility

Zhao, Yan; Yao, Yiyu; Luo, Fei

doi:10.1016/j.ins.2007.06.031

Cited by 114 publications

(49 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, for our further development, we briefly review some basic notions of Pawlak rough set 1 and the grade indiscernibility relation 18 . Meanwhile, the traditional non-incremental algorithm of calculating approximations is presented.…”

Section: Preliminariesmentioning

confidence: 99%

“…The difference has not been described in Pawlak's indiscernibility relation. To address this issue, Zhao proposed the grade indiscernibility relation for information system 18 , which is defined as follows. …”

Section: The Grade Indiscernibility Relationmentioning

confidence: 99%

“…Incremental update method can improve the efficiency by using the existing approximation knowledge 18 .…”

Section: Incrementally Updating Approximations While Adding or Removimentioning

confidence: 99%

“…Zhao proposed the notion of grade indiscernibility relation which is a fuzzy relation for information system to characterize the difference between the grades of discernibility 18 . Based on fuzzy rough set model 8 , Qin investigated rough approximation operators based on the grade indiscernibility relation 19 .…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Incremental update of rough set approximation under the grade indiscernibility relation

Luo¹,

Liu²,

Qin³

et al. 2017

IJCIS

View full text Add to dashboard Cite

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.

show abstract

Section: Preliminariesmentioning

confidence: 99%

Section: The Grade Indiscernibility Relationmentioning

confidence: 99%

“…Incremental update method can improve the efficiency by using the existing approximation knowledge 18 .…”

Section: Incrementally Updating Approximations While Adding or Removimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Incremental update of rough set approximation under the grade indiscernibility relation

Luo¹,

Liu²,

Qin³

et al. 2017

IJCIS

View full text Add to dashboard Cite

show abstract

“…[2,11,31,42,43,56,60]). A minimal set of attributes that preserves the decision making power of the original system is called a decision reduct.…”

Section: Introductionmentioning

confidence: 99%

Attribute selection with fuzzy decision reducts

Cornelis

Jensen

Hurtado

et al. 2010

Information Sciences

219

View full text Add to dashboard Cite

Rough set theory provides a methodology for data analysis based on the approximation of concepts in information systems. It revolves around the notion of discernibility: the ability to distinguish between objects, based on their attribute values. It allows to infer data dependencies that are useful in the fields of feature selection and decision model construction. In many cases, however, it is more natural, and more effective, to consider a gradual notion of discernibility. Therefore, within the context of fuzzy rough set theory, we present a generalization of the classical rough set framework for databased attribute selection and reduction using fuzzy tolerance relations. The paper unifies existing work in this direction, and introduces the concept of fuzzy decision reducts, dependent on an increasing attribute subset measure. Experimental results demonstrate the potential of fuzzy decision reducts to discover shorter attribute subsets, leading to decision models with a better coverage and with comparable, or even higher accuracy.

show abstract

Value reducts and bireducts: A comparative study

Chacón-Gómez

Cornejo

Medina

et al. 2022

Math Methods in App Sciences

View full text Add to dashboard Cite

In Rough Set Theory, the notion of bireduct allows to simultaneously reduce the sets of objects and attributes contained in a dataset. In addition, value reducts are used to remove some unnecessary values of certain attributes for a specific object. Therefore, the combination of both notions provides a higher reduction of unnecessary data. This paper is focused on the study of bireducts and value reducts of information and decision tables. We present theoretical results capturing different aspects about the relationship between bireducts and reducts, offering new insights at a conceptual level. We also analyze the relationship between bireducts and value reducts. The studied connections among these notions provide important profits for the efficient information analysis, as well as for the detection of unnecessary or redundant information.

show abstract

Data analysis based on discernibility and indiscernibility

Cited by 114 publications

References 26 publications

Incremental update of rough set approximation under the grade indiscernibility relation

Incremental update of rough set approximation under the grade indiscernibility relation

Attribute selection with fuzzy decision reducts

Value reducts and bireducts: A comparative study

Contact Info

Product

Resources

About