Minimum cost attribute reduction in decision-theoretic rough set models

Jia, Xiuyi; Liao, Wenhe; Tang, Zhenmin; Shang, Lin

doi:10.1016/j.ins.2012.07.010

Cited by 198 publications

(74 citation statements)

References 27 publications

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“…In this paper, we make a generalization that attributes may have different numbers of levels of granulations. Since various rough set models have been proposed for knowledge reduction and decision rules acquisition in information tables [4,7,[9][10][11]15,21,23,32,42,45,49] , optimal scale selection of the general multi-scale information systems is mainly studied in this paper. Besides, we only discuss consistent decision tables since an inconsistent decision table can be transformed into a consistent one.…”

Section: Introductionmentioning

confidence: 99%

A new approach of optimal scale selection to multi-scale decision tables

2017

Information Sciences

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Section: Introductionmentioning

confidence: 99%

A new approach of optimal scale selection to multi-scale decision tables

2017

Information Sciences

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“…Through the three decades of the development, rough set has been demonstrated to be useful in knowledge acquisition 5,10 , pattern recognition 3,7,23 , machine learning 6,8,9,11,26 , decision support 21,44,46,57 and so on.…”

Section: Introductionmentioning

confidence: 99%

Multigranulation rough set: A multiset based strategy

Yang¹,

Xu²,

Dou³

et al. 2017

IJCIS

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A simple multigranulation rough set approach is to approximate the target through a family of binary relations. Optimistic and pessimistic multigranulation rough sets are two typical examples of such approach. However, these two multigranulation rough sets do not take frequencies of occurrences of containments or intersections into account. To solve such problem, by the motivation of the multiset, the model of the multiple multigranulation rough set is proposed, in which both lower and upper approximations are multisets. Such two multisets are useful when counting frequencies of occurrences such that objects belong to lower or upper approximations with a family of binary relations. Furthermore, not only the concept of approximate distribution reduct is introduced into multiple multigranulation rough set, but also a heuristic algorithm is presented for computing reduct. Finally, multiple multigranulation rough set approach is tested on eight UCI (University of California-Irvine) data sets. Experimental results show: 1. the approximate quality based on multiple multigranulation rough set is between approximate qualities based on optimistic and pessimistic multigranulation rough sets; 2. by comparing with optimistic and pessimistic multigranulation rough sets, multiple multigranulation rough set needs more attributes to form a reduct.

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“…(DTRS) [2,7,8,[10][11][12][13][14][15][16][17][18][19][20][21][22]26,27,[32][33][34][35][36]38] extended RST by considering probabilistic information of objects into a set of positive, negative or boundary regions, by introducing a pair of thresholds (a, b). When proper thresholds are used, we can derive several existing probabilistic rough set models, such as the variable precision rough set (VPRS) [40,41], the game-theoretic rough set (GTRS) [2,12], and the Bayesian rough set (BRS) [23] from the decision-theoretic rough set model.…”

Section: Introductionmentioning

confidence: 99%