A fuzzy support vector machine with qualitative regression preset

Yicheng Wei; Junzo Watada; Witold Pedrycz

doi:10.1109/ICGEC.2012.15

A fuzzy support vector machine with qualitative regression preset

Yicheng Wei^*, Junzo Watada, Witold Pedrycz

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

In this paper, we formulate a qualitative classification model by means of qualitative fuzzy regression presetbased fuzzy support vector machine (FQR-FSVM). This new model will make it possible to achieve discrimination of output while characterizing membership for each class in terms of multi-dimensional qualitative inputs (attributes). Moreover, the new model will largely shorten the computing time especially for large database by using linear preset of fuzzy qualitative regression classifier to limit the non-linear classification region.

Original language	English
Title of host publication	Proceedings - 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012
Pages	393-399
Number of pages	7
DOIs	https://doi.org/10.1109/ICGEC.2012.15
Publication status	Published - 2012
Event	2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012 - Kitakyushu Duration: 2012 Aug 25 → 2012 Aug 28

Other

Other	2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012
City	Kitakyushu
Period	12/8/25 → 12/8/28

Keywords

Fuzzy qualitative regression classifier
Fuzzy support vector machine (FSVM)
Qualitative classification

ASJC Scopus subject areas

Biochemistry, Genetics and Molecular Biology(all)

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10.1109/ICGEC.2012.15

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Wei, Y, Watada, J & Pedrycz, W 2012, A fuzzy support vector machine with qualitative regression preset. in Proceedings - 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012., 6457100, pp. 393-399, 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012, Kitakyushu, 12/8/25. https://doi.org/10.1109/ICGEC.2012.15

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abstract = "In this paper, we formulate a qualitative classification model by means of qualitative fuzzy regression presetbased fuzzy support vector machine (FQR-FSVM). This new model will make it possible to achieve discrimination of output while characterizing membership for each class in terms of multi-dimensional qualitative inputs (attributes). Moreover, the new model will largely shorten the computing time especially for large database by using linear preset of fuzzy qualitative regression classifier to limit the non-linear classification region.",

keywords = "Fuzzy qualitative regression classifier, Fuzzy support vector machine (FSVM), Qualitative classification",

author = "Yicheng Wei and Junzo Watada and Witold Pedrycz",

year = "2012",

doi = "10.1109/ICGEC.2012.15",

language = "English",

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pages = "393--399",

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note = "2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012 ; Conference date: 25-08-2012 Through 28-08-2012",

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AU - Watada, Junzo

AU - Pedrycz, Witold

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AB - In this paper, we formulate a qualitative classification model by means of qualitative fuzzy regression presetbased fuzzy support vector machine (FQR-FSVM). This new model will make it possible to achieve discrimination of output while characterizing membership for each class in terms of multi-dimensional qualitative inputs (attributes). Moreover, the new model will largely shorten the computing time especially for large database by using linear preset of fuzzy qualitative regression classifier to limit the non-linear classification region.

KW - Fuzzy qualitative regression classifier

KW - Fuzzy support vector machine (FSVM)

KW - Qualitative classification

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M3 - Conference contribution

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BT - Proceedings - 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012

T2 - 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012

Y2 - 25 August 2012 through 28 August 2012

ER -

A fuzzy support vector machine with qualitative regression preset

Abstract

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Keywords

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