On Robust Fuzzy Rough Set Models

Hu, Qinghua; Zhang, Lei; An, Shuang; Zhang, D.; Yu, Daren

doi:10.1109/tfuzz.2011.2181180

Cited by 146 publications

(56 citation statements)

References 54 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

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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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“…This can be a disadvantage in a data analysis context, since data samples may be erroneous. Such noisy data can perturb the approximations and therefore weaken the machine learning algorithms that invoke them [27].…”

Section: Robust Fuzzy Rough Set Modelsmentioning

confidence: 99%

“…Moreover, some models use other aggregation operators than the infimum and supremum operators [7,18]. To the best of our knowledge, the seven models we discuss here are the most widely used robust fuzzy rough set models [27,65].…”

Section: Robust Fuzzy Rough Set Modelsmentioning

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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“…Based on the outcome of the decision tree algorithm, either four (30,31,32,33) or five (22,30,31,32,33) features are removed in the constructed trees. The main reason for removal is that features are numerical and some are used repeatedly.…”

Section: Datasets and Selected Featuresmentioning

confidence: 99%

Classification of BGP anomalies using decision trees and fuzzy rough sets

Xing

Hua

et al. 2014

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Abstract-Border Gateway Protocol (BGP) is the core component of the Internet's routing infrastructure. Abnormal routing behavior impairs global Internet connectivity and stability. Hence, designing and implementing anomaly detection algorithms is important for improving performance of routing protocols. While various machine learning techniques may be employed to detect BGP anomalies, their performance strongly depends on the employed learning algorithms. These techniques have multiple variants that often work well for detecting a particular anomaly. In this paper, we use the decision tree and fuzzy rough set methods for feature selection. Decision tree and extreme learning machine classification techniques are then used to maximize the accuracy of detecting BGP anomalies. The proposed techniques are tested using Internet traffic traces.

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On Robust Fuzzy Rough Set Models

Cited by 146 publications

References 54 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

Classification of BGP anomalies using decision trees and fuzzy rough sets

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