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A Forward Variable Selection Method for Fuzzy Logistic Regression

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Abstract

The logistic regression analysis is a popular method for describing the relation between variables. However, when there are a big number of variables in the regression model, the selection of the best model becomes a major problem. In this condition, the question is which subset of predictors can best predict the response pattern, and which process can be used to achieve such a subset. This article is written to answer this questioning fuzzy logistic regression models. To this end, based on the existing criteria of regression models, three goodness-of-fit criteria, namely MSEF, AICF, and \(C_{p}^{\text{F}}\), are proposed. These criteria are helpful to select the best-fitted model among all possible fuzzy logistic regression models with fuzzy covariates and responses. In addition, based on the concepts of efficiency level and MSEF, a forward model selection method for fuzzy logistic regression is proposed. The proposed method is justified by some simulation studies, indicating the good performance and efficiency of the method. In addition, we applied the presented methods in a clinical trial study.

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Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, Faculty of Health, Birjand University of Medical Science, Birjand, Iran

    Fatemeh Salmani

  2. School of Engineering Science, College of Engineering, University of Tehran, Tehran, 1417613131, Iran

    Seyed Mahmoud Taheri

  3. Department of Community Medicine, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Alireza Abadi

Authors
  1. Fatemeh Salmani
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  2. Seyed Mahmoud Taheri
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  3. Alireza Abadi
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Correspondence to Seyed Mahmoud Taheri.

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Salmani, F., Taheri, S.M. & Abadi, A. A Forward Variable Selection Method for Fuzzy Logistic Regression. Int. J. Fuzzy Syst. 21, 1259–1269 (2019). https://doi.org/10.1007/s40815-019-00615-z

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  • DOI: https://doi.org/10.1007/s40815-019-00615-z

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