Fuzzy decision-making SVM with an offset for real-world lopsided data classification

Boyang Li*, Jinglu Hu, Kotaro Hirasawa

*Corresponding author for this work

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

2 Citations (Scopus)


An improved support vector machine (SVM) classifier model for classifying the real-world lopsided data is proposed. The most obvious differences between the model proposed and conventional SVM classifiers are the designs of decision-making functions and the introduction of an offset parameter. With considering about the vagueness of the real-world data sets, a fuzzy decision-making function is designed to take the place of the traditional sign function in the prediction part of SVM classifier. Because of the existence of the interaction and noises influence around the boundary between different clusters, this flexible design of decision-making model which is more similar to the real-world situations can present better performances. In addition, in this paper we mainly discuss an offset parameter introduced to modify the boundary excursion caused by the imbalance of the real-world datasets. Because noises in the real-world can also influence the separation boundary, a weighted harmonic mean (WHM) method is used to modify the offset parameter. Due to these improvements, more robust performances are presented in our simulations.

Original languageEnglish
Title of host publication2006 SICE-ICASE International Joint Conference
Number of pages6
Publication statusPublished - 2006 Dec 1
Event2006 SICE-ICASE International Joint Conference - Busan, Korea, Republic of
Duration: 2006 Oct 182006 Oct 21

Publication series

Name2006 SICE-ICASE International Joint Conference


Conference2006 SICE-ICASE International Joint Conference
Country/TerritoryKorea, Republic of


  • Classification
  • Fuzzy decision-making function
  • Real-world lopsided dataset
  • SVM
  • WHM offset

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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