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- Glad Deschrijver, Chris Cornelis, Etienne E. Kerre
- IEEE Trans. Fuzzy Systems
- 2004

Intuitionistic fuzzy sets form an extension of fuzzy sets: while fuzzy sets give a degree to which an element belongs to a set, intuitionistic fuzzy sets give both a membership degree and a nonmembership degree. The only constraint on those two degrees is that their sum must be smaller than or equal to 1. In fuzzy set theory, an important class of… (More)

- Chris Cornelis, Glad Deschrijver, Etienne E. Kerre
- Int. J. Approx. Reasoning
- 2004

With the demand for knowledge-handling systems capable of dealing with and distinguishing between various facets of imprecision ever increasing, a clear and formal characterization of the mathematical models implementing such services is quintessential. In this paper, this task is undertaken simultaneously for the definition of implication within two… (More)

- Chris Cornelis, Martine De Cock, Anna Maria Radzikowska
- RSFDGrC
- 2007

The hybridization of rough sets and fuzzy sets has focused on creating an end product that extends both contributing computing paradigms in a conservative way. As a result, the hybrid theory inherits their respective strengths, but also exhibits some weaknesses. In particular, although they allow for gradual membership, fuzzy rough sets are still abrupt in… (More)

- Patricia Victor, Chris Cornelis, Martine De Cock, Ankur Teredesai
- IEEE Intelligent Systems
- 2011

The paper is discussing well-known trust enhanced information filtering techniques for recommending controversial reviews by the recommender systems.

- Richard Jensen, Chris Cornelis
- RSCTC
- 2008

In this paper, we present a new fuzzy-rough nearest neighbour (FRNN) classification algorithm, as an alternative to Sarkar’s fuzzyrough ownership function (FRNN-O) approach. By contrast to the latter, our method uses the nearest neighbours to construct lower and upper approximations of decision classes, and classifies test instances based on their… (More)

- Richard Jensen, Chris Cornelis
- Theor. Comput. Sci.
- 2011

In this paper, we propose a nearest neighbour algorithm that uses the lower and upper approximations from fuzzy rough set theory in order to classify test objects, or predict their decision value. It is shown experimentally that our method outperforms other nearest neighbour approaches (classical, fuzzy and fuzzy-rough ones) and that it is competitive with… (More)

- Chris Cornelis, Richard Jensen, Germán Hurtado Martín, Dominik Slezak
- Inf. Sci.
- 2010

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… (More)

- Martine De Cock, Chris Cornelis, Etienne E. Kerre
- IEEE Trans. Fuzzy Systems
- 2007

Traditional rough set theory uses equivalence relations to compute lower and upper approximations of sets. The corresponding equivalence classes either coincide or are disjoint. This behaviour is lost when moving on to a fuzzy T-equivalence relation. However, none of the existing studies on fuzzy rough set theory tries to exploit the fact that an element… (More)

- Patricia Victor, Chris Cornelis, Martine De Cock, Paulo Pinheiro
- Fuzzy Sets and Systems
- 2009

Trust networks among users of a recommender system (RS) prove beneficial to the quality and amount of the recommendations. Since trust is often a gradual phenomenon, fuzzy relations are the pre-eminent tools for modeling such networks. However, as current trust-enhanced RSs do not work with the notion of distrust, they cannot differentiate unknown users… (More)

- Joaquín Derrac, Chris Cornelis, Salvador García, Francisco Herrera
- Inf. Sci.
- 2012

In recent years, fuzzy rough set theory has emerged as a suitable tool for performing feature selection. Fuzzy rough feature selection enables us to analyze the discernibility of the attributes, highlighting the most attractive features in the construction of classifiers. However, its results can be enhanced even more if other data reduction techniques,… (More)