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Support vector logistic regression model with exact predictors and fuzzy responses

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Abstract

A simple method is proposed in this paper for estimating the fuzzy logistic regression model adopted with support vector machines. The proposed method is robust against the outliers in cases that the predictors are exact quantities and the responses are fuzzy data. For this purpose, the unknown center, left, and right spreads of fuzzy regression coefficients were estimated via a separated three-stage estimation procedure. The performance of the proposed method was also compared with similar methods in terms of some common goodness-of-fit criteria used in fuzzy regression analysis. The numerical results revealed that the proposed fuzzy (non-linear) logistic regression model can offer sufficiently accurate results compared to other methods.

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Acknowledgements

The authors would like to thank the editor and anonymous reviewers for their constructive suggestions and comments, which improved the presentation of this work.

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Authors and Affiliations

  1. Department of Statistics, Payame Noor University, Tehran, 19395-3697, Iran

    Gh. Hesamian

  2. Department of Statistics, University of Birjand, Birjand, 615-97175, Iran

    M. G. Akbari

Authors
  1. Gh. Hesamian
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  2. M. G. Akbari
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Correspondence to Gh. Hesamian.

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Appendix: Tables

Appendix: Tables

See Tables 4, 5, 6, 7, 8, and 9.

Table 4 Data set in Example 1
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Table 5 Fuzzy linguistic term sets and their corresponding TIFNs of outputs (SLE disease) in Example 1
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Table 6 Estimated fuzzy coefficients corresponding to the proposed method and some common fuzzy logistic regression models in Example 1
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Table 7 Fuzzy linguistic term sets and their corresponding TIFNs of outputs (SLE disease) in Example 2
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Table 8 Data set in Example 2
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Table 9 Estimated fuzzy coefficients corresponding to the proposed method and some common fuzzy logistic regression models in Example 2
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Hesamian, G., Akbari, M.G. Support vector logistic regression model with exact predictors and fuzzy responses. J Ambient Intell Human Comput 14, 817–828 (2023). https://doi.org/10.1007/s12652-021-03333-3

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