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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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References

  • Akbari MG, Hesamian G (2019) A partial-robust-ridge-based regression model with fuzzy predictors-responses. J Comput Appl Math 351:290–301

    Article  MATH  Google Scholar 

  • Balasundaram S, Meena Y (2019) Robust support vector regression in primal with asymmetric Huber loss. Neural Process Lett 49:1399–1431

    Article  Google Scholar 

  • Coppi R, D’Urso P, Giordani P, Santoro A (2006) Least squares estimation of a linear regression model with \(LR\)-fuzzy response. Comput Stat Data Anal 51:267–286

    Article  MATH  Google Scholar 

  • Gao Y, Lu Q (2018) A fuzzy logistic regression model based on the least squares estimation. Comput Appl Math 37:3562–3579

    Article  MATH  Google Scholar 

  • Gu B, Sheng WS, Wang Z, Ho D, Osman S, Li S (2015) Incremental learning for v-support vector regression. Neural Netw 67:140–150

    Article  MATH  Google Scholar 

  • Gu B, Sheng WS, Tay KY, Romano R, Li S (2017) Cross validation through two-dimensional solution surface for cost-sensitive SVM. IEEE Trans Pattern Anal Mach Intell 39:1103–1121

    Article  Google Scholar 

  • Györfi L, Kohler M, Krzyzak A, Walk H (2002) A distribution-free theory of non-parametric regression. Springer, New York

    Book  MATH  Google Scholar 

  • Hastie T, Tibshirani R, Friedman J (2008) The elements of statistical learning. Springer, Berlin

    MATH  Google Scholar 

  • Hesamian G, Akbari M (2017) Semi-parametric partially logistic regression model with exact inputs and intuitionistic fuzzy outputs. Appl Soft Comput 58:517–526

    Article  Google Scholar 

  • Hesamian G, Akbari MG, Asadollahi M (2017) Fuzzy semi-parametric partially linear model with fuzzy inputs and fuzzy outputs. Expert Syst Appl 71:230–239

    Article  Google Scholar 

  • Lee KH (2005) First course on fuzzy theory and applications. Springer, Berlin

    MATH  Google Scholar 

  • Namdari M, Yoon JH, Abadi A, Taheri SM, Choi SH (2015) Fuzzy logistic regression with least absolute deviations estimators. Soft Comput 19:909917

    Article  Google Scholar 

  • Pourahmad S, Ayatollahi SMT, Taheri SM, Habib Agahi Z (2011a) Fuzzy logistic regression based on the least squares approach with application in clinical studies. Comput Math Appl 62:3353–3365

    Article  MATH  Google Scholar 

  • Pourahmad S, Ayatollahi SMT, Taheri SM (2011b) Fuzzy logistic regression: a new possibilistic model and its application in clinical vague status. Iran J Fuzzy Syst 8:1–17

    MATH  Google Scholar 

  • Taheri SM, Abadi A, Namdari M, Esmaillzadeh A, Sarbakhsh P (2016) Using fuzzy logistic regression for modeling vague status situations: application to a dietary pattern study. Intell Decis Technol 10:183–192

    Article  Google Scholar 

  • Takemura K (2004) Fuzzy logistic regression analysis for fuzzy input–output data. In: The Proceedings of the second international symposium on soft computing and intelligent system

  • Tanaka H, Uejima S, Kiyoji A (1982) Linear regression analysis with fuzzy model. IEEE Trans Syst Man Cybern 12:903–907

    Article  MATH  Google Scholar 

  • Trott M (2004) The mathematica guidebook for programming. Springer, New York

    Book  MATH  Google Scholar 

  • Vapnik VN (1995) The nature of statistical learning theory. Springer, Berlin

    Book  MATH  Google Scholar 

  • Vapnik VN (1998) Statistical learning theory. Wiley, New York

    MATH  Google Scholar 

  • Wasserman L (2007) All of non-parametric statistics. Springer, New York

    Google Scholar 

Download references

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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  • DOI: https://doi.org/10.1007/s12652-021-03333-3

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