A Fuzzy Support Vector Machine with Qualitative Regression Preset

Author

Yicheng Wei ; Watada, Junzo ; Pedrycz, Witold

Author_Institution

Grad. Sch. of Inf., Waseda Univ., Kitakyushu, Japan

fYear

2012

fDate

25-28 Aug. 2012

Firstpage

393

Lastpage

399

Abstract

In this paper, we formulate a qualitative classification model by means of qualitative fuzzy regression preset based 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 set theory; pattern classification; regression analysis; support vector machines; FQR-FSVM; fuzzy support vector machine; large database; multidimensional qualitative inputs; nonlinear classification region; qualitative classification model; qualitative regression preset; Accuracy; Computational modeling; Databases; Fuzzy sets; Mathematical model; Support vector machines; Training; Fuzzy Qualitative Regression classifier; Fuzzy Support Vector Machine (FSVM); Qualitative classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Genetic and Evolutionary Computing (ICGEC), 2012 Sixth International Conference on

Conference_Location

Kitakushu

Print_ISBN

978-1-4673-2138-9

Type

conf

DOI

10.1109/ICGEC.2012.15

Filename

6457100