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An Integrated approach for fuzzy logistic regression

Cilt: 11 Sayı: 1 29 Haziran 2018
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An Integrated approach for fuzzy logistic regression

Öz

The aim of this study is to introduced an integrated fuzzy logistic regression approach to describe the relationship between crisp inputs and fuzzy binary output. For this reason, we integrated the fuzzy logistic regression methods proposed by Pourahmad et al. [17]  and Sohn et al. [24] to define a possibility measure for each case and then used the logarithmic transformation of possibilistic odds as fuzzy output observations. To estimate the parameters of the fuzzy logistic regression model, Diamond [5]’s Fuzzy Least Squares (FLS) approach is used. A numerical example is presented and obtained results are compared with classic logistic regression model.

Anahtar Kelimeler

Kaynakça

  1. [1] B.L. Aswathi, 2009, Sensitivity, Specificity, Accuracy and the relationship between them, in: Bioinformatics.
  2. [2] A. Celmiņš, 1987, Least squares model fitting to fuzzy vector data, Fuzzy Sets and Systems, 22, 245-269.
  3. [3] P.T. Chang, E.S. Lee, 1994, Fuzzy linear regression with spreads unrestricted in sign, Computers & Mathematics with Applications, 28, 61-70.
  4. [4] P. D'Urso, 2003, Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data, Computational Statistics & Data Analysis, 42, 47-72.
  5. [5] P. Diamond, 1988, Fuzzy least squares, Information Sciences, 46, 141-157.
  6. [6] M. Hojati, C.R. Bector, K. Smimou, A simple method for computation of fuzzy linear regression, European Journal of Operational Research, 166 (2005) 172-184.
  7. [7] C. Kao, C.-L. Chyu, 2002, A fuzzy linear regression model with better explanatory power, Fuzzy Sets and Systems, 126, 401-409.
  8. [8] U.T. Khan, C. Valeo, 2015, A new fuzzy linear regression approach for dissolved oxygen prediction, Hydrological Sciences Journal, 60, 1096-1119.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Mühendislik

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

29 Haziran 2018

Gönderilme Tarihi

22 Nisan 2018

Kabul Tarihi

24 Haziran 2018

Yayımlandığı Sayı

Yıl 2018 Cilt: 11 Sayı: 1

Kaynak Göster

APA
Pehlivan, N. Y., & Şahin, A. (2018). An Integrated approach for fuzzy logistic regression. İstatistikçiler Dergisi:İstatistik ve Aktüerya, 11(1), 42-54. https://izlik.org/JA85DW36BX
AMA
1.Pehlivan NY, Şahin A. An Integrated approach for fuzzy logistic regression. JSSA. 2018;11(1):42-54. https://izlik.org/JA85DW36BX
Chicago
Pehlivan, Nimet Yapıcı, ve Aynur Şahin. 2018. “An Integrated approach for fuzzy logistic regression”. İstatistikçiler Dergisi:İstatistik ve Aktüerya 11 (1): 42-54. https://izlik.org/JA85DW36BX.
EndNote
Pehlivan NY, Şahin A (01 Haziran 2018) An Integrated approach for fuzzy logistic regression. İstatistikçiler Dergisi:İstatistik ve Aktüerya 11 1 42–54.
IEEE
[1]N. Y. Pehlivan ve A. Şahin, “An Integrated approach for fuzzy logistic regression”, JSSA, c. 11, sy 1, ss. 42–54, Haz. 2018, [çevrimiçi]. Erişim adresi: https://izlik.org/JA85DW36BX
ISNAD
Pehlivan, Nimet Yapıcı - Şahin, Aynur. “An Integrated approach for fuzzy logistic regression”. İstatistikçiler Dergisi:İstatistik ve Aktüerya 11/1 (01 Haziran 2018): 42-54. https://izlik.org/JA85DW36BX.
JAMA
1.Pehlivan NY, Şahin A. An Integrated approach for fuzzy logistic regression. JSSA. 2018;11:42–54.
MLA
Pehlivan, Nimet Yapıcı, ve Aynur Şahin. “An Integrated approach for fuzzy logistic regression”. İstatistikçiler Dergisi:İstatistik ve Aktüerya, c. 11, sy 1, Haziran 2018, ss. 42-54, https://izlik.org/JA85DW36BX.
Vancouver
1.Nimet Yapıcı Pehlivan, Aynur Şahin. An Integrated approach for fuzzy logistic regression. JSSA [Internet]. 01 Haziran 2018;11(1):42-54. Erişim adresi: https://izlik.org/JA85DW36BX