OWA-FRPS: A Prototype Selection Method Based on Ordered Weighted Average Fuzzy Rough Set Theory

Verbiest, Nele; Cornelis, Chris; Herrera, Francisco

doi:10.1007/978-3-642-41218-9_19

Cited by 16 publications

(13 citation statements)

References 22 publications

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“…While simple rules of thumb have been used for this purpose so far (see e.g. [7,58]), a more systematic study of their generation is mandatory, e.g., by using learning methods as proposed in [3,19,55]. This study is left for future research.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…The best performing one out of the generated set constitutes the final result of the IS method. The method was further optimized in [140], using an alternative quality measure, dispensing with the granularity parameter.…”

Section: Fuzzy Rough Is Methodsmentioning

confidence: 99%

Applications of Fuzzy Rough Set Theory in Machine Learning: a Survey

Vluymans

D'eer

Saeys

et al. 2015

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Data used in machine learning applications is prone to contain both vague and incomplete information. Many authors have proposed to use fuzzy rough set theory in the development of new techniques tackling these characteristics. Fuzzy sets deal with vague data, while rough sets allow to model incomplete information. As such, the hybrid setting of the two paradigms is an ideal candidate tool to confront the separate challenges. In this paper, we present a thorough review on the use of fuzzy rough sets in machine learning applications. We recall their integration in preprocessing methods and consider learning algorithms in the supervised, unsupervised and semi-supervised domains and outline future challenges. Throughout the paper, we highlight the interaction between theoretical advances on fuzzy rough sets and practical machine learning tools that take advantage of them.

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“…These are in turn defined, for any x, y ∈ X, as the complement of the difference between the attribute values x a and y a after rescaling by the sample standard deviation σ a (1). There are a number of differences between the implementations in [15] and [14]. In each case, the present implementation follows [14]:…”

Section: Fuzzy Rough Feature Selection (Frfs)mentioning

confidence: 99%

“…Only a limited number of fuzzy rough set machine learning algorithms have received publicly available software implementations. Variants of Fuzzy Rough Nearest Neighbours (FRNN) [5], Fuzzy Rough Rule Induction [6], Fuzzy Rough Feature Selection (FRFS) [1] and Fuzzy Rough Prototype Selection (FRPS) [14,15] are included in the R package RoughSets [12], and have also been released for use with the Java machine learning software suite WEKA [3,4].…”

Section: Introductionmentioning

confidence: 99%

fuzzy-rough-learn 0.1: A Python Library for Machine Learning with Fuzzy Rough Sets

2020

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We present fuzzy-rough-learn, the first Python library of fuzzy rough set machine learning algorithms. It contains three algorithms previously implemented in R and Java, as well as two new algorithms from the recent literature. We briefly discuss the use cases of fuzzy-roughlearn and the design philosophy guiding its development, before providing an overview of the included algorithms and their parameters.

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OWA-FRPS: A Prototype Selection Method Based on Ordered Weighted Average Fuzzy Rough Set Theory

Cited by 16 publications

References 22 publications

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

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

Applications of Fuzzy Rough Set Theory in Machine Learning: a Survey

fuzzy-rough-learn 0.1: A Python Library for Machine Learning with Fuzzy Rough Sets

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