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July  2015, 11(3): 921-932. doi: 10.3934/jimo.2015.11.921

Clustering based polyhedral conic functions algorithm in classification

 1 Department of Industrial Engineering, Faculty of Engineering, Anadolu University, Eskisehir, 26555, Turkey 2 Vitra, Eczacibasi Yapi Gerecleri, 11300 Bilecik, Turkey

Received  October 2013 Revised  June 2014 Published  October 2014

In this study, a new algorithm based on polyhedral conic functions (PCFs) is developed to solve multi-class supervised data classification problems. The $k$ PCFs are constructed for each class in order to separate it from the rest of the data set. The $k$-means algorithm is applied to find vertices of PCFs and then a linear programming model is solved to calculate the parameters of each PCF. The separating functions for each class are obtained as a pointwise minimum of the PCFs. A class label is assigned to the test point according to its minimum value over all separating functions. In order to demonstrate the performance of the proposed algorithm, it is applied to solve classification problems in publicly available data sets. The comparative results with some mainstream classifiers are presented.
Citation: Gurkan Ozturk, Mehmet Tahir Ciftci. Clustering based polyhedral conic functions algorithm in classification. Journal of Industrial & Management Optimization, 2015, 11 (3) : 921-932. doi: 10.3934/jimo.2015.11.921
References:
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show all references

References:
 [1] A. Astorino and M. Gaudioso, Polyhedral separability through successive LP,, Journal of Optimization Theory and Applications, 112 (2002), 265. doi: 10.1023/A:1013649822153. [2] A. Astorino, M. Gaudioso and A. Seeger, Conic separation of finite sets. i: The homogeneous case,, Journal of Convex Analysis, 21 (2014), 001. [3] K. Bache and M. Lichman, UCI machine learning repository, 2013., URL , (). [4] A. M. Bagirov, Max-min separability,, Optimization Methods and Software, 20 (2005), 277. doi: 10.1080/10556780512331318263. [5] A. M. Bagirov and J. Ugon, Supervised data classification via max-min separability,, Applied Optimization, 99 (2005), 175. doi: 10.1007/0-387-26771-9_6. [6] A. M. Bagirov, M. Ghosh and D. Webb, A derivative-free method for linearly constrained nonsmooth optimization,, Journal of Industrial and Management Optimization, 2 (2006), 319. [7] A. M. Bagirov, J. Ugon, D. Webb, G. Ozturk and R. Kasimbeyli, A novel piecewise linear classifier based on polyhedral conic and max-min separabilities,, TOP, 21 (2013), 3. doi: 10.1007/s11750-011-0241-5. [8] C. J. C. Burges, A tutorial on support vector machines for pattern recognition,, Data Mining and Knowledge Discovery, 2 (1998), 121. [9] R. N. Gasimov and G. Ozturk, Separation via polihedral conic functions,, Optimization Methods and Software, 21 (2006), 527. doi: 10.1080/10556780600723252. [10] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten, The weka data mining software: An update,, SIGKDD Explorations, 11 (2009), 10. doi: 10.1145/1656274.1656278. [11] R. Kasimbeyli, Radial epiderivatives and set-valued optimization,, Optimization, 58 (2009), 521. doi: 10.1080/02331930902928310. [12] R. Kasimbeyli, A nonlinear cone separation theorem and scalarization in nonconvex vector optimization,, SIAM J. on Optimization, 20 (2009), 1591. doi: 10.1137/070694089. [13] R. Kasimbeyli and M. Mammadov, On weak subdifferentials, directional derivatives, and radial epiderivatives for nonconvex functions,, SIAM Journal on Optimization, 20 (2009), 841. doi: 10.1137/080738106. [14] G. Ozturk, A New Mathematical Programming Approach to Solve Classification Problems,, PhD thesis, 6 (2007). [15] R. Rosenthal, GAMS: A User's Guide,, GAMS Development Corporation, (2013). [16] K. Schittkowski, Optimal parameter selection in support vector machines,, Journal of Industrial and Management Optimization, 1 (2005), 465. doi: 10.3934/jimo.2005.1.465.
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