Volume 7, Issue 1, February 2014, Pages 172 - 185
Multivariate Least Squares Regression using Interval-Valued Fuzzy Data and based on Extended Yao-Wu Signed Distance
Authors
Faezeh Torkian, Mohsen Arefi, Mohammad Ghasem Akbari
Corresponding Author
Mohsen Arefi
Received 18 November 2012, Accepted 16 June 2013, Available Online 3 February 2014.
- DOI
- 10.1080/18756891.2013.859867How to use a DOI?
- Keywords
- Coefficient of multiple determination, Goodness of fit, Interval-valued fuzzy set, Multivariate least squares regression, Similarity measure
- Abstract
The purpose of this study is to introduce a new regression model, based on the least squares method, when the available data of both explanatory variable(s) and response variable are interval-valued fuzzy (IVF) numbers. The proposed method is based on a new metric on the space of IVF numbers, which is an extended version of the signed distance introduced by Yao and Wu (2000). In order to evaluate the goodness of fit of the proposed model, we introduce some new indices based on the similarity measure and the coefficient of multiple determination. Finally, the application of proposed approach is provided to model some real data.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Faezeh Torkian AU - Mohsen Arefi AU - Mohammad Ghasem Akbari PY - 2014 DA - 2014/02/03 TI - Multivariate Least Squares Regression using Interval-Valued Fuzzy Data and based on Extended Yao-Wu Signed Distance JO - International Journal of Computational Intelligence Systems SP - 172 EP - 185 VL - 7 IS - 1 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2013.859867 DO - 10.1080/18756891.2013.859867 ID - Torkian2014 ER -