Article Outline
Introduction
MILP Formulation
Training Problem Formulation
Testing Problem Formulation
Application
Conclusion
References
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References
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
Bajgier SM, Hill AV (1982) An experimental comparison of statistical and linear programming approaches to the discriminant problem. Decis Sci 13:604–618
Cawley G (2000) Matlab Support Vector Machine Toolbox. School of Information Systems, University of East Anglia
Chen MS, Han J, Yu PS (1996) Data Mining: An overview from a database perspective. IEEE Trans Knowl Data Eng 8:866–883
Cortes C, Vapnik V (1995) Support vector network. Mach Learn 20:273–297
Edelstein H (2003) Building Profitable Customer Relationships with Data Mining. Two Crows Corporation, Maryland
Erenguc SS, Koehler GJ (1990) Survey of mathematical programming models and experimental results for linear discriminant analysis. Manag Decis Econ 11:215–225
Gehrlein WV (1986) General mathematical programming formulations for the statistical classification problem. Oper Res Lett 5(6):299–304
iData Analyzer, Version 2.0, Information Acumen Corporation.
Jagota A (2000) Data Analysis and Classification for Bioinformatics, Bay Press, New York
Joachimsthaler EA, Stam A (1990) Mathematical programming approaches for the classification problem in two-group discriminant analysis. Multivar Behav Res 25:427–454
Koehler GJ (1990) Considerations for mathematical programming models in discriminant analysis. Manag Decis Econ 11:227–234
Littschwager JM, Wang C (1978) Integer programming solution of a classification problem. Manag Sci 24(14):1515–1525
Roiger RJ, Geatz MW (2003) Data Mining – A Tutorial Based Primer. Addison Wesley Press, Boston
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
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
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
Uney F, Turkay M (2006) A Mixed-Integer Programming Approach to Multi-Class Data Classification Problem. Eur J Oper Res 173(3):910–920
Vapnik VN (1998) Statistical Learning Theory. Wiley, New York
WEKA (Waikato Environment for Knowledge Analysis) (199–2005) Version 3.4.5, University of Waikato, New Zealand
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
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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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DOI: https://doi.org/10.1007/978-0-387-74759-0_406
Publisher Name: Springer, Boston, MA
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