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

Boyang Li, Jinglu Hu, Kotaro Hirasawa, Pu Sun, Kenneth Marko

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルInternational Joint Conference on Neural Networks 2006, IJCNN '06
ページ587-592
ページ数6
出版ステータスPublished - 2006 12 1
イベントInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
継続期間: 2006 7 162006 7 21

出版物シリーズ

名前IEEE International Conference on Neural Networks - Conference Proceedings
ISSN(印刷版)1098-7576

Conference

ConferenceInternational Joint Conference on Neural Networks 2006, IJCNN '06
国/地域Canada
CityVancouver, BC
Period06/7/1606/7/21

ASJC Scopus subject areas

  • ソフトウェア

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