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] B.L. Aswathi, 2009, Sensitivity, Specificity, Accuracy and the relationship between them, in: Bioinformatics.
- [2] A. Celmiņš, 1987, Least squares model fitting to fuzzy vector data, Fuzzy Sets and Systems, 22, 245-269.
- [3] P.T. Chang, E.S. Lee, 1994, Fuzzy linear regression with spreads unrestricted in sign, Computers & Mathematics with Applications, 28, 61-70.
- [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] P. Diamond, 1988, Fuzzy least squares, Information Sciences, 46, 141-157.
- [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] C. Kao, C.-L. Chyu, 2002, A fuzzy linear regression model with better explanatory power, Fuzzy Sets and Systems, 126, 401-409.
- [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