Convex hull approach to fuzzy regression analysis and its application to oral age model

Junzo Watada*, Yoshihiro Toyoura, Seung Gook Hwang

*Corresponding author for this work

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

11 Citations (Scopus)

Abstract

Fuzzy multivariate analyses including linear regression analysis, fuzzy time-series analysis, fuzzy possibilistic linear model, etc. are formulated in terms of the extension principle. One objective of a fuzzy linear regression model is to build a model using fuzzy which represent the possibilities included in the system. In this paper in order that we can overcome this issue, we propose the effective method to decrease drastically the number of samples in terms of convex hull method. In this paper stress is placed on that (1) only vertex points obtained by the convex hull are constraints on a linear programming on that (2) the convex hull approach works efficiently in real-time data gathering, and on that (3) the number of vertexes obtained by the convex hull will not increase so much. Using a numerical example, we show difference between both the convex hull and the conventional approaches to decrease the number of samples in building the model. We also illustrate and build the oral age model using real data about the age and the number of sound teeth based on Convex Hull.

Original languageEnglish
Title of host publicationAnnual Conference of the North American Fuzzy Information Processing Society - NAFIPS
EditorsM.H. Smith, W.A. Gruver, L.O. Hall
Pages867-871
Number of pages5
Volume2
Publication statusPublished - 2001
Externally publishedYes
EventJoint 9th IFSA World Congress and 20th NAFIPS International Conference - Vancouver, BC
Duration: 2001 Jul 252001 Jul 28

Other

OtherJoint 9th IFSA World Congress and 20th NAFIPS International Conference
CityVancouver, BC
Period01/7/2501/7/28

Keywords

  • Convex hull
  • Efficient solution
  • Fuzzy regression analysis
  • Gift wrapping method
  • Oral age model

ASJC Scopus subject areas

  • Computer Science(all)
  • Media Technology

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