Solving fuzzy linear regression with hybrid optimization

M. H. Mashinchi; M. A. Orgun; M. Mashinchi

doi:10.1007/978-3-642-10684-2_37

Solving fuzzy linear regression with hybrid optimization

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

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

5 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	16th International Conference, ICONIP 2009, Bangkok, Thailand, December 1-5, 2009, Proceedings, Part II
Editors	Chi Sing Leung, Minho Lee, Jonathan H. Chan
Place of Publication	Berlin
Publisher	Springer, Springer Nature
Pages	336-343
Number of pages	8
Edition	PART 2
ISBN (Electronic)	9783642106842
ISBN (Print)	9783642106828
DOIs	https://doi.org/10.1007/978-3-642-10684-2_37
Publication status	Published - 2009
Event	16th International Conference on Neural Information Processing, ICONIP 2009 - Bangkok, Thailand Duration: 1 Dec 2009 → 5 Dec 2009

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Berlin Heidelberg
Volume	5864
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	16th International Conference on Neural Information Processing, ICONIP 2009
Country/Territory	Thailand
City	Bangkok
Period	1/12/09 → 5/12/09

Access to Document

10.1007/978-3-642-10684-2_37

Cite this

Mashinchi, M. H., Orgun, M. A., & Mashinchi, M. (2009). Solving fuzzy linear regression with hybrid optimization. In C. S. Leung, M. Lee, & J. H. Chan (Eds.), Neural Information Processing: 16th International Conference, ICONIP 2009, Bangkok, Thailand, December 1-5, 2009, Proceedings, Part II (PART 2 ed., pp. 336-343). (Lecture Notes in Computer Science; Vol. 5864). Springer, Springer Nature. https://doi.org/10.1007/978-3-642-10684-2_37

Mashinchi, M. H. ; Orgun, M. A. ; Mashinchi, M. / Solving fuzzy linear regression with hybrid optimization. Neural Information Processing: 16th International Conference, ICONIP 2009, Bangkok, Thailand, December 1-5, 2009, Proceedings, Part II. editor / Chi Sing Leung ; Minho Lee ; Jonathan H. Chan. PART 2. ed. Berlin : Springer, Springer Nature, 2009. pp. 336-343 (Lecture Notes in Computer Science).

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abstract = "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.",

author = "Mashinchi, {M. H.} and Orgun, {M. A.} and M. Mashinchi",

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Mashinchi, MH, Orgun, MA & Mashinchi, M 2009, Solving fuzzy linear regression with hybrid optimization. in CS Leung, M Lee & JH Chan (eds), Neural Information Processing: 16th International Conference, ICONIP 2009, Bangkok, Thailand, December 1-5, 2009, Proceedings, Part II. PART 2 edn, Lecture Notes in Computer Science, vol. 5864, Springer, Springer Nature, Berlin, pp. 336-343, 16th International Conference on Neural Information Processing, ICONIP 2009, Bangkok, Thailand, 1/12/09. https://doi.org/10.1007/978-3-642-10684-2_37

Solving fuzzy linear regression with hybrid optimization. / Mashinchi, M. H.; Orgun, M. A.; Mashinchi, M.

Neural Information Processing: 16th International Conference, ICONIP 2009, Bangkok, Thailand, December 1-5, 2009, Proceedings, Part II. ed. / Chi Sing Leung; Minho Lee; Jonathan H. Chan. PART 2. ed. Berlin : Springer, Springer Nature, 2009. p. 336-343 (Lecture Notes in Computer Science; Vol. 5864).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Solving fuzzy linear regression with hybrid optimization

AU - Mashinchi, M. H.

AU - Orgun, M. A.

AU - Mashinchi, M.

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

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SN - 9783642106828

T3 - Lecture Notes in Computer Science

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EP - 343

BT - Neural Information Processing

A2 - Leung, Chi Sing

A2 - Lee, Minho

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PB - Springer, Springer Nature

CY - Berlin

T2 - 16th International Conference on Neural Information Processing, ICONIP 2009

Y2 - 1 December 2009 through 5 December 2009

ER -

Mashinchi MH, Orgun MA, Mashinchi M. Solving fuzzy linear regression with hybrid optimization. In Leung CS, Lee M, Chan JH, editors, Neural Information Processing: 16th International Conference, ICONIP 2009, Bangkok, Thailand, December 1-5, 2009, Proceedings, Part II. PART 2 ed. Berlin: Springer, Springer Nature. 2009. p. 336-343. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-642-10684-2_37

Solving fuzzy linear regression with hybrid optimization

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