Solving fuzzy linear regression with hybrid optimization

M. H. Mashinchi, M. A. Orgun, M. Mashinchi

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

5 Citations (Scopus)


Fuzzy linear regression is an important tool to find the linear inexact relationship between uncertain data. We then propose a hybrid optimization method based on tabu search and harmony search as a potential way of solving fuzzy linear regression. The proposed method aims at finding a model without considering any mathematical constraints while reducing the error of the regression's model in comparison to other methods. The experimental comparison of the results for two classes of crisp input-fuzzy output and fuzzy input-fuzzy output data sets shows the superiority of the method over the existing ones.

Original languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publication16th International Conference, ICONIP 2009, Bangkok, Thailand, December 1-5, 2009, Proceedings, Part II
EditorsChi Sing Leung, Minho Lee, Jonathan H. Chan
Place of PublicationBerlin
PublisherSpringer, Springer Nature
Number of pages8
EditionPART 2
ISBN (Electronic)9783642106842
ISBN (Print)9783642106828
Publication statusPublished - 2009
Event16th International Conference on Neural Information Processing, ICONIP 2009 - Bangkok, Thailand
Duration: 1 Dec 20095 Dec 2009

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other16th International Conference on Neural Information Processing, ICONIP 2009


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