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Multi-Class Data Classification via Mixed-Integer Optimization

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Encyclopedia of Optimization

Article Outline

Introduction

MILP Formulation

  Training Problem Formulation

  Testing Problem Formulation

Application

Conclusion

References

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References

  1. Adem J, Gochet W (2006) Mathematical programming based heuristics for improving LP-generated classifiers for the multi-class supervised classification problem. Eur J Oper Res 168:181–199

    Article  MathSciNet  MATH  Google Scholar 

  2. Bajgier SM, Hill AV (1982) An experimental comparison of statistical and linear programming approaches to the discriminant problem. Decis Sci 13:604–618

    Google Scholar 

  3. Cawley G (2000) Matlab Support Vector Machine Toolbox. School of Information Systems, University of East Anglia

    Google Scholar 

  4. Chen MS, Han J, Yu PS (1996) Data Mining: An overview from a database perspective. IEEE Trans Knowl Data Eng 8:866–883

    Article  Google Scholar 

  5. Cortes C, Vapnik V (1995) Support vector network. Mach Learn 20:273–297

    MATH  Google Scholar 

  6. Edelstein H (2003) Building Profitable Customer Relationships with Data Mining. Two Crows Corporation, Maryland

    Google Scholar 

  7. Erenguc SS, Koehler GJ (1990) Survey of mathematical programming models and experimental results for linear discriminant analysis. Manag Decis Econ 11:215–225

    Article  Google Scholar 

  8. Gehrlein WV (1986) General mathematical programming formulations for the statistical classification problem. Oper Res Lett 5(6):299–304

    Article  MathSciNet  Google Scholar 

  9. iData Analyzer, Version 2.0, Information Acumen Corporation.

    Google Scholar 

  10. Jagota A (2000) Data Analysis and Classification for Bioinformatics, Bay Press, New York

    Google Scholar 

  11. Joachimsthaler EA, Stam A (1990) Mathematical programming approaches for the classification problem in two-group discriminant analysis. Multivar Behav Res 25:427–454

    Article  Google Scholar 

  12. Koehler GJ (1990) Considerations for mathematical programming models in discriminant analysis. Manag Decis Econ 11:227–234

    Article  Google Scholar 

  13. Littschwager JM, Wang C (1978) Integer programming solution of a classification problem. Manag Sci 24(14):1515–1525

    Article  Google Scholar 

  14. Roiger RJ, Geatz MW (2003) Data Mining – A Tutorial Based Primer. Addison Wesley Press, Boston

    Google Scholar 

  15. Stam A, Joachimsthaler EA (1990) A comparison of a robust mixed-integer approach to existing methods for establishing classification rules for the discriminant problem. Eur J Oper Res 46(1):113–122

    Article  MATH  Google Scholar 

  16. Tax D, Duin R (2002) Using two-class classifiers for multi class classification, Proc 16th Int Conference Pattern Recogn, Quebec City, Canada, vol II, IEEE Computers Society Press, Los Alamitos, pp 124–127

    Google Scholar 

  17. Turkay M, Uney F, Yilmaz O (2005) Prediction of Folding type of Proteins Using Mixed-Integer Linear Programming. In: Puigjaner L, Espuna A (eds) Computer-Aided Chem. Eng. vol 20A: ESCAPE-15. Elsevier, Amsterdam, pp 523–528

    Google Scholar 

  18. Uney F, Turkay M (2006) A Mixed-Integer Programming Approach to Multi-Class Data Classification Problem. Eur J Oper Res 173(3):910–920

    Article  MathSciNet  Google Scholar 

  19. Vapnik VN (1998) Statistical Learning Theory. Wiley, New York

    MATH  Google Scholar 

  20. WEKA (Waikato Environment for Knowledge Analysis) (199–2005) Version 3.4.5, University of Waikato, New Zealand

    Google Scholar 

  21. Weiss SM, Kulikowski CA (1991) Computer systems that learn: classification and prediction methods from statistics, neural networks, machine learning and expert systems. Morgan Kaufmann, San Mateo, CA

    Google Scholar 

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Türkay, M., Yüksektepe, F.Ü. (2008). Multi-Class Data Classification via Mixed-Integer Optimization . In: Floudas, C., Pardalos, P. (eds) Encyclopedia of Optimization. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74759-0_406

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