Fuzzy Representations in Rough Set Approximations

Hadjimichael, Michael; Wong, S. K. M.

doi:10.1007/978-1-4471-3238-7_41

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…Other extensions to standard rough set theory have been suggested in Refs. [22][23][24][25]. In the present paper, fuzzy information systems defined by Definition 2 are proposed to handle uncertain data.…”

Section: Fuzzy Information Systemsmentioning

confidence: 99%

Interpreting and extracting fuzzy decision rules from fuzzy information systems and their inference

Pei

Resconi²,

Wal³

et al. 2006

Information Sciences

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Section: Fuzzy Information Systemsmentioning

confidence: 99%

Interpreting and extracting fuzzy decision rules from fuzzy information systems and their inference

Pei

Resconi²,

Wal³

et al. 2006

Information Sciences

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“…Different extensions of the standard rough set theory have been suggested in [2], [4], [16]. We consider that values of attributes may be vague, for instance, high, low, medium, etc., so, the domain of each attributes is a set of linguistic terms represented by membership functions to specify how to evaluate the truth of the attribute value on the learning examples.…”

Section: Alpha-reducts Are Generalized Reductsmentioning

confidence: 99%

Induction of strong feature subsets

Quafafou

Boussouf

1997

Lecture Notes in Computer Science

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A b s t r a c t The problem of features subset selection can be defined as the selection of a relevant subset of features which allows a leanaing algorithm to induce small high-accuracy concepts. To achieve this goal, two main approaches have been developed, the first one is algorithm-independent (filter approach) which considers only the data, when the second approach takes into account both the data and a given learning algorithm (wrapper approach). Recent work were developed to study the interest of rough set theory and more particularly its notions of reducts and core to deal with the problem of features subset selection. Different methods were proposed to select features using both the core and the reduct concepts, whereas other researches show that useful features subset does not necessarily contain all features in cores. In this paper, we underline the fact that rough set theory is concerned with deterministic analysis of attribute dependencies which are at basis of the two notions of reduct and core. We extend the notion of dependency which allows us to find both deterministic and non-deterministic dependencies. A new notion of strong reducts is then introduced and leads to the definition of strong feature subsets (SFS). The interest of SFS is illustrated by the improvement of the accuracy of our learning system, called Alpha, on real-world datasets.

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“…The research on fuzzy information is involved with fuzzy information caused by fuzzy scale. Fuzzy information is divided into individual fuzzy information and group fuzzy information [1][2][3][4][5] . For information processing, the information is considered as a triple -object, property and property value.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Representation for Rough Set Space

Chen

Xiong

2009

2009 First International Conference on Information Science and Engineering

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Rough set and fuzzy set theory in information system is the research on incomplete knowledge, the problem is not accurate. According to the limitation of traditional knowledge representation system using fixed attribute set to describe object, the fuzzy model is formulated to describe information in knowledge representation system and the model provides the fuzzy representation of objects in database and their approximation space.

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Fuzzy Representations in Rough Set Approximations

Cited by 6 publications

References 9 publications

Interpreting and extracting fuzzy decision rules from fuzzy information systems and their inference

Interpreting and extracting fuzzy decision rules from fuzzy information systems and their inference

Induction of strong feature subsets

Fuzzy Representation for Rough Set Space

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