The Model of Fuzzy Variable Precision Rough Sets

Zhao, Suyun; Tsang, Eric W. K.; Chen, Degang

doi:10.1109/icmlc.2007.4370673

Cited by 26 publications

(48 citation statements)

References 46 publications

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“…An important challenge is to extend the formal treatment to noise-tolerant fuzzy rough set models, such as those studied in [23][24][25][26][27][28][29]. Observing that the implicator-conjunctor based approximations are sensitive to small changes in the arguments (for instance, because of their reliance on inf and sup operations), many authors have proposed models that are more robust against data perturbation.…”

Section: Discussionmentioning

confidence: 99%

Implicator-Conjunctor Based Models of Fuzzy Rough Sets: Definitions and Properties

D'eer

Verbiest

Cornelis

et al. 2013

Lecture Notes in Computer Science

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Abstract. Ever since the first hybrid fuzzy rough set model was proposed in the early 1990's, many researchers have focused on the definition of the lower and upper approximation of a fuzzy set by means of a fuzzy relation. In this paper, we review those proposals which generalize the logical connectives and quantifiers present in the rough set approximations by means of corresponding fuzzy logic operations. We introduce a general model which encapsulates all of these proposals, evaluate it w.r.t. a number of desirable properties, and refine the existing axiomatic approach to characterize lower and upper approximation operators.

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Section: Discussionmentioning

confidence: 99%

Implicator-Conjunctor Based Models of Fuzzy Rough Sets: Definitions and Properties

D'eer

Verbiest

Cornelis

et al. 2013

Lecture Notes in Computer Science

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“…To mitigate this problem in the crisp case, Ziarko [70] proposed the Variable Precision Rough Set (VPRS) model in 1993. This model also served as a starting point for the design of several noise-tolerant fuzzy rough set approaches, such as [5,7,17,18,24,25,42,43,65,69], which will be discussed in detail in Section 4.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive study of implicator–conjunctor-based and noise-tolerant fuzzy rough sets: Definitions, properties and robustness analysis

D'eer

Verbiest

Cornelis

et al. 2015

Fuzzy Sets and Systems

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Both rough and fuzzy set theories offer interesting tools for dealing with imperfect data: while the former allows us to work with uncertain and incomplete information, the latter provides a formal setting for vague concepts. The two theories are highly compatible, and since the late 1980s many researchers have studied their hybridization. In this paper, we critically evaluate most relevant fuzzy rough set models proposed in the literature. To this end, we establish a formally correct and unified mathematical framework for them. Both implicator-conjunctor-based definitions and noise-tolerant models are studied. We evaluate these models on two different fronts: firstly, we discuss which properties of the original rough set model can be maintained and secondly, we examine how robust they are against both class and attribute noise. By highlighting the benefits and drawbacks of the different fuzzy rough set models, this study appears a necessary first step to propose and develop new models in future research.

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“…However, the major drawback of the classical methods is that the optimal subset is not guaranteed to be found by either a theoretical or practical approach. Therefore, fuzzy rough sets have become a popular tool for discovering the optimal or near-optimal subset [1]. Fuzzy rough set is advocated for handling real attributes, discrete attributes, or mixtures of both.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Algorithm for Finding a Fuzzy Rough Set Reduct Using an Improved Harmony Search

Daoud

2015

IJMECS

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Abstract-To increase learning accuracy, it is important to remove misleading, redundant, and irrelevant features. Fuzzy rough set offers formal mathematical tools to reduce the number of attributes and determine the minimal subset. Unfortunately, using the formal approach is time consuming, particularly if a large dataset is used. In this paper, an efficient algorithm for finding a reduct is introduced. Several techniques are proposed and combined with the harmony search, such as using a balanced fitness function, fusing the classical ranking methods with the fuzzy-rough method, and applying binary operations to speed up implementation. Comprehensive experiments on 18 datasets demonstrate the efficiency of using the suggested algorithm and show that the new algorithm outperforms several well-known algorithms.

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The Model of Fuzzy Variable Precision Rough Sets

Cited by 26 publications

References 46 publications

Implicator-Conjunctor Based Models of Fuzzy Rough Sets: Definitions and Properties

Implicator-Conjunctor Based Models of Fuzzy Rough Sets: Definitions and Properties

A comprehensive study of implicator–conjunctor-based and noise-tolerant fuzzy rough sets: Definitions, properties and robustness analysis

An Efficient Algorithm for Finding a Fuzzy Rough Set Reduct Using an Improved Harmony Search

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