Support vector machine with fuzzy decision-making for real-world data classification

Boyang Li, Takayuki Furuzuki, Kotaro Hirasawa, Pu Sun, Kenneth Marko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper proposes an improved model for the application of support vector machine (SVM) to achieve the real-world data classification. Being different from traditional SVM classifiers, the new model takes the thought about fuzzy theory into account. And a fuzzy decision-making function is also built to replace the sign function in the prediction stage of classification process. In the prediction part, the method proposed uses the decision value as the independent variable of fuzzy decision-making function to classify test data set into different classes, but not only the sign of which. This flexible design of decision-making model more approaches to the properties of real-world conditions in which interaction and noise influence exist around the boundary between different clusters. So many misclassifled cases can be modified when these sets are considered as fuzzy ones. In addition, a boundary offset is also introduced to modify the excursion produced by the imbalance of real-world dataset. Then an improved and more robust performance will be presented by using this adjustable fuzzy decision-making SVM model in simulations.

Original languageEnglish
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
Pages587-592
Number of pages6
Publication statusPublished - 2006
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC
Duration: 2006 Jul 162006 Jul 21

Other

OtherInternational Joint Conference on Neural Networks 2006, IJCNN '06
CityVancouver, BC
Period06/7/1606/7/21

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ASJC Scopus subject areas

  • Software

Cite this

Li, B., Furuzuki, T., Hirasawa, K., Sun, P., & Marko, K. (2006). Support vector machine with fuzzy decision-making for real-world data classification. In IEEE International Conference on Neural Networks - Conference Proceedings (pp. 587-592). [1716146]