Rough–fuzzy functions in classification

Sarkar, Manish

doi:10.1016/s0165-0114(02)00119-7

Cited by 63 publications

(20 citation statements)

References 25 publications

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“…Sakar [73] generalizes the concept of rough membership functions in pattern classification tasks to rough-fuzzy membership functions and rough-fuzzy ownership functions. Unlike the rough membership value of a pattern, which is sensitive only toward the rough uncertainty associated with the pattern, the rough-fuzzy membership (or ownership) value of the pattern signifies the rough uncertainty as well as the fuzzy uncertainty associated with the pattern.…”

Section: B Rough-fuzzy Hybridization In Medical Imagingmentioning

confidence: 99%

Rough Sets and Near Sets in Medical Imaging: A Review

Hassanien¹,

Abraham²,

Peters³

et al. 2009

IEEE Trans. Inform. Technol. Biomed.

148

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Abstract-This paper presents a review of the current literature on rough set and near set-based approaches to solving various problems in medical imaging such as medical image segmentation, object extraction and image classification. Rough set frameworks hybridized with other computational intelligence technologies that include neural networks, particle swarm optimization, support vector machines and fuzzy sets are also presented. In addition, a brief introduction to near sets and near images with an application to MRI images is given. Near sets offer a generalization of traditional rough set theory and a new approach to classifying perceptual objects by means of features in solving medical imaging problems. Challenges to be addressed and future directions of research are identified and an extensive bibliography is also included.

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Section: B Rough-fuzzy Hybridization In Medical Imagingmentioning

confidence: 99%

Rough Sets and Near Sets in Medical Imaging: A Review

Hassanien¹,

Abraham²,

Peters³

et al. 2009

IEEE Trans. Inform. Technol. Biomed.

148

View full text Add to dashboard Cite

show abstract

“…Sakar [36] generalizes the concept of rough membership functions in pattern classification tasks to rough-fuzzy membership functions and rough-fuzzy ownership functions. Unlike the rough membership value of a pattern, which is sensitive only toward the rough uncertainty associated with the pattern, the rough-fuzzy membership (or ownership) value of the pattern signifies the rough uncertainty as well as the fuzzy uncertainty associated with the pattern.…”

Section: Rough Set-fuzzy Set Approaches In Image Processingmentioning

confidence: 99%

An overview of rough-hybrid approaches in image processing

Hassanien

Abraham

Peters

et al. 2008

2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)

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Abstract-Rough set theory offers a novel approach to manage uncertainty that has been used for the discovery of data dependencies, importance of features, patterns in sample data, feature space dimensionality reduction, and the classification of objects. Consequently, rough sets have been successfully employed for various image processing tasks including image segmentation, enhancement and classification. Nevertheless, while rough sets on their own provide a powerful technique, it is often the combination with other computational intelligence techniques that results in a truly effective approach. In this paper we show how rough sets have been combined with various other methodologies such as neural networks, wavelets, mathematical morphology, fuzzy sets, genetic algorithms, bayesian approaches, swarm optimization, and support vector machines in the image processing domain.

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“…Rough sets are applied in many domains, e.g., in medicine, economics, information technology, telecommunication, and marketing [1][2][3][4][5][6][7][8][9][10][11][12][13]. Several publications reveal the need to extend the traditional rough set approach and to integrate it with other methods and conceptions [5,[14][15][16][17][18][19][20]. Therefore, the idea of using color spectrum sets for expert knowledge extraction is introduced.…”

Section: Introductionmentioning

confidence: 99%

Integration of the Visible Color Spectrum and the Rough Sets Theory in the Process of Expert Knowledge Extraction

Pilipczuk¹

2013

IJCA

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This paper presents the basic problems encountered in the process of expert knowledge extraction. Several experiments were carried out to compare the effectiveness of using linguistic and color data during this process. Particular attention was given to the color spectrum sets, and an example of how they can be applied in the rough sets theory is provided. The main purpose of the paper is to prove that the color spectrum scale more closely reflects people's opinions than the linguistic scale does and that it is possible to effectively integrate the color spectrum scale with the rough sets theory to extract expert knowledge. The experiments were conducted on a group of real estate specialists to obtain knowledge rules during the process of buying a building plot. Opinions were collected by questionnaire and interview. The experiments confirmed the hypothesis.

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Rough–fuzzy functions in classification

Cited by 63 publications

References 25 publications

Rough Sets and Near Sets in Medical Imaging: A Review

Rough Sets and Near Sets in Medical Imaging: A Review

An overview of rough-hybrid approaches in image processing

Integration of the Visible Color Spectrum and the Rough Sets Theory in the Process of Expert Knowledge Extraction

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