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Veri Zarflama Analizi Tabanlı Yeni Bir Hibrid İki Gruplu Sınıflandırma Modeli

Year 2015, Volume: 19 Issue: 3, - , 03.12.2015

H. Hasan Örkcü , Mustafa İsa Doğan

https://doi.org/10.19113/sdufbed.13457
Cited By: 1

Abstract

Bu çalışmada iki gruplu sınıflandırma problemlerinin çözümünde kullanılabilecek yeni bir sınıflandırma modeli geliştirilmiştir. Bu model Veri Zarflama Analizi BCC modeline dayanan Pendharkar ve Troutt (2014) modeli ile Sueyoshi (2004) tarafından önerilen iki aşamalı sınıflandırma modelinin bir karmasıdır. Çalışmanın amacı, BCC modelindeki parçalı doğrusal etkinlik sınırı ve iki aşamalı detaylı inceleme fikri sayesinde iki gruplu sınıflandırma problemlerini ele almaktır. Önerilen yeni yaklaşım Pendharkar ve Troutt (2014)’den alınan bir örnek üzerinde ayrıntılı olarak incelenmiş ve ayrıca yapılan simülasyon çalışmasından önerilen yöntemin sınıflandırma performansının diğer iki yöntemden daha iyi olduğu gözlenmiştir.

Keywords

Sınıflandırma problemi; Veri Zarflama Analizi; BCC modeli; İki aşamalı yaklaşım

References

  • Anderson, T.W., 1984. “An introduction to multivariate analysis”, Wiley, New York, USA, 10-25.
  • Bal, H., Örkcü, H.H., 2005. “Combining the Discriminant Analysis and Data Envelopment Analysis in view of Multiple Criteria Decision Making: A New Model”, G.U. Journal of Science, 18 (3), 355-364.
  • Bal, H., Örkcü, H.H., Çelebioğlu S., 2006a. “An Experimental Comparison of the New Goal Programming and Linear Programming Approaches in the Two-Group Discriminant Problems”, Computers&Industrial Engineering, 50 (3), 296-311.
  • Bal, H., Örkcü, H.H., Çelebioğlu S., 2006b. "An Alternative Model to Fisher and Linear Programming Approaches in Two-Group Classification Problem: Minimizing Deviations from the Group Median", G.U. Journal of Science, 19 (1), 49-55.
  • Bal, H., Örkcü, H.H., 2007. “Data Envelopment Analysis Approach to Two-Group Classification Problems and an Experimantal Comparison with Some Classification Models”, Hacettepe Journal of Mathematics and Statistics, 36(2), 169-180.
  • Bal, H., Örkcü, H.H., 2011. “A new mathematical programming approach to Multi-Group Classification Problems”, Computers and Operations Research, 38(1), 105-111.
  • Banker, R.D., Charnes, A., Cooper, W.W., 1984. “Some models for estimating technical and scale inefficiencies in data envelopment analysis”, Management Science, 30(9): 1078-1092.
  • Charnes, A., Cooper, W.W., Rhodes, E., 1978. “Measuring the efficiency of decision making units”, European Journal of Operational Research, 2: 429-444.
  • Cooper, W.W., Seiford, L.M., Tone, K., 2000. “Data envelopment analysis”, Kluwer Academic Publishers, Boston USA., 25-60.
  • Fisher, R., 1936. “The use of multiple measurements in taxonomic problems”, Annals of Eugenics, 7 (2): 179-188.
  • Fred, N., Glover, F., 1981a. “A Linear programming approach to the discriminant problem”, Decision Sciences, 12: 68-74.
  • Fred, N., Glover, F., 1981b. “Simple but powerful goal programming formulations for the statistical discriminant problem”, European Journal of Operational Research, 7: 44-60
  • Fred, N., Glover, F., 1986a. “Evaluating alternative linear programming models to solve the two-group discriminant problem”, Decision Sciences, 17: 151-162.
  • Fred, N., Glover, F., 1986b. “Resolving certain difficulties and improving the classification power of LP discriminant analysis formulations”, Decision Sciences, 17: 589-595.
  • Glover, F. 1990., “Improving linear programming models for the discriminant problem”, Decision Sciences, 21: 771-785.
  • Hosseini, J.H., Armacost, R.L., 1994. “Two-group discriminant problem with equal group mean vectors: An experimental evaluation of six linear/nonlinear programming formulations”, European Journal of Operational Research, 77: 241-252.
  • Joachimsthaler, E.A., Stam, A., 1988. “Four approaches to the classification problem in discriminant analysis: An experimental study”, Decision Sciences, 19: 322-333.
  • Koehler, G.J., 1989. “Characterization of unacceptable solutions in LP discriminant analysis”, Decision Sciences, 21: 239-257.
  • Koehler, G.J., Erenguc, S.S., 1990. “Minimizing misclassifications in linear discriminant problem”, Decision Sciences, 21: 63-85.
  • Markowski, E.P., Markowski, C.A., 1985. “Some difficulties and improvements in applying linear programming formulations to the discriminant problem”, Decision Sciences, 16: 237-247.
  • Lachenburch P.A., 1975.“Discriminant analysis”, Hafner Press, New York, USA, 40-90.
  • Lam, K.F., Choo, E.U., Moy, J.W., 1996. “Minimizing deviations from the group mean: A new linear programming approach for the two-group classification problem”, European Journal of Operational Research, 88: 358-367.
  • Lam, K.F., Moy, J.W., 1996. “Improved linear programming formulations for the multi-group discriminant problem”, Journal of Operational Research Society, 47: 1526-1529.
  • Lam, K.F., Moy, J.W., 1997. “An experimental comparison of some linear programming approaches to the discriminant problem”, Computers and Operations Research, 24 (7), 593-599.
  • Lam, K.F., Moy, J.W., 2002. “Combining discriminant method in solving classification problems in two-group discriminant analysis”, European Journal of Operational Research, 138: 294-301.
  • Lee, C.K., Ord, J.K., 1990. “Disciminant analysis using least absolute deviations”, Decision Sciences, 21: 86-96 .
  • Örkcü, H.H., Bal, H., 2011. “A Combining Mathematical Programming Method for Multi-Group Data Classification”, G.U. Journal of Science, 24 (1), 77-84.
  • Pendharkar, P.C., 2011. “A hybrid radial basis function and data envelopment analysis neural network classification”, Computers and Operations Research, 38 (1): 256-266.
  • Pendharkar, P.C. and Troutt, M.D., 2014. “Interactive classification using data envelopment analysis”, Annals of Operations Research, 214 (1): 125-141.
  • Rubin, A., 1990. “A comparison of linear programming and parametric approaches to the two-group discriminant problem”, Decision Sciences, 21: 373-386.
  • Sueyoshi, T., 1999. “DEA-Discriminant analysis in the view of goal programming”, European Journal of Operational Research, 115: 564-582.
  • Sueyoshi, T., 2001. “Extended DEA-Discriminant analysis”, European Journal of Operational Research, 131: 324-351.
  • Sueyoshi, T., 2004. “Mixed integer programming approach of extended DEA-Discriminant analysis”, European Journal of Operational Research, 152: 45-55.
  • Stam, A., Jones, D.G., 1990. “Classification performance of mathematical programming techniques in discriminant analysis: Results for small and medium sample sizes”, Managerial and Decision Economics, 11: 243-253.
  • Stam, A., Ragsdale, C.T., 1992. “On the classification gap in mathematical programming based approaches to the discriminant problem”, Naval Research Logistic, 39: 545-559.

Year 2015, Volume: 19 Issue: 3, - , 03.12.2015

H. Hasan Örkcü , Mustafa İsa Doğan

https://doi.org/10.19113/sdufbed.13457
Cited By: 1

Abstract

References

  • Anderson, T.W., 1984. “An introduction to multivariate analysis”, Wiley, New York, USA, 10-25.
  • Bal, H., Örkcü, H.H., 2005. “Combining the Discriminant Analysis and Data Envelopment Analysis in view of Multiple Criteria Decision Making: A New Model”, G.U. Journal of Science, 18 (3), 355-364.
  • Bal, H., Örkcü, H.H., Çelebioğlu S., 2006a. “An Experimental Comparison of the New Goal Programming and Linear Programming Approaches in the Two-Group Discriminant Problems”, Computers&Industrial Engineering, 50 (3), 296-311.
  • Bal, H., Örkcü, H.H., Çelebioğlu S., 2006b. "An Alternative Model to Fisher and Linear Programming Approaches in Two-Group Classification Problem: Minimizing Deviations from the Group Median", G.U. Journal of Science, 19 (1), 49-55.
  • Bal, H., Örkcü, H.H., 2007. “Data Envelopment Analysis Approach to Two-Group Classification Problems and an Experimantal Comparison with Some Classification Models”, Hacettepe Journal of Mathematics and Statistics, 36(2), 169-180.
  • Bal, H., Örkcü, H.H., 2011. “A new mathematical programming approach to Multi-Group Classification Problems”, Computers and Operations Research, 38(1), 105-111.
  • Banker, R.D., Charnes, A., Cooper, W.W., 1984. “Some models for estimating technical and scale inefficiencies in data envelopment analysis”, Management Science, 30(9): 1078-1092.
  • Charnes, A., Cooper, W.W., Rhodes, E., 1978. “Measuring the efficiency of decision making units”, European Journal of Operational Research, 2: 429-444.
  • Cooper, W.W., Seiford, L.M., Tone, K., 2000. “Data envelopment analysis”, Kluwer Academic Publishers, Boston USA., 25-60.
  • Fisher, R., 1936. “The use of multiple measurements in taxonomic problems”, Annals of Eugenics, 7 (2): 179-188.
  • Fred, N., Glover, F., 1981a. “A Linear programming approach to the discriminant problem”, Decision Sciences, 12: 68-74.
  • Fred, N., Glover, F., 1981b. “Simple but powerful goal programming formulations for the statistical discriminant problem”, European Journal of Operational Research, 7: 44-60
  • Fred, N., Glover, F., 1986a. “Evaluating alternative linear programming models to solve the two-group discriminant problem”, Decision Sciences, 17: 151-162.
  • Fred, N., Glover, F., 1986b. “Resolving certain difficulties and improving the classification power of LP discriminant analysis formulations”, Decision Sciences, 17: 589-595.
  • Glover, F. 1990., “Improving linear programming models for the discriminant problem”, Decision Sciences, 21: 771-785.
  • Hosseini, J.H., Armacost, R.L., 1994. “Two-group discriminant problem with equal group mean vectors: An experimental evaluation of six linear/nonlinear programming formulations”, European Journal of Operational Research, 77: 241-252.
  • Joachimsthaler, E.A., Stam, A., 1988. “Four approaches to the classification problem in discriminant analysis: An experimental study”, Decision Sciences, 19: 322-333.
  • Koehler, G.J., 1989. “Characterization of unacceptable solutions in LP discriminant analysis”, Decision Sciences, 21: 239-257.
  • Koehler, G.J., Erenguc, S.S., 1990. “Minimizing misclassifications in linear discriminant problem”, Decision Sciences, 21: 63-85.
  • Markowski, E.P., Markowski, C.A., 1985. “Some difficulties and improvements in applying linear programming formulations to the discriminant problem”, Decision Sciences, 16: 237-247.
  • Lachenburch P.A., 1975.“Discriminant analysis”, Hafner Press, New York, USA, 40-90.
  • Lam, K.F., Choo, E.U., Moy, J.W., 1996. “Minimizing deviations from the group mean: A new linear programming approach for the two-group classification problem”, European Journal of Operational Research, 88: 358-367.
  • Lam, K.F., Moy, J.W., 1996. “Improved linear programming formulations for the multi-group discriminant problem”, Journal of Operational Research Society, 47: 1526-1529.
  • Lam, K.F., Moy, J.W., 1997. “An experimental comparison of some linear programming approaches to the discriminant problem”, Computers and Operations Research, 24 (7), 593-599.
  • Lam, K.F., Moy, J.W., 2002. “Combining discriminant method in solving classification problems in two-group discriminant analysis”, European Journal of Operational Research, 138: 294-301.
  • Lee, C.K., Ord, J.K., 1990. “Disciminant analysis using least absolute deviations”, Decision Sciences, 21: 86-96 .
  • Örkcü, H.H., Bal, H., 2011. “A Combining Mathematical Programming Method for Multi-Group Data Classification”, G.U. Journal of Science, 24 (1), 77-84.
  • Pendharkar, P.C., 2011. “A hybrid radial basis function and data envelopment analysis neural network classification”, Computers and Operations Research, 38 (1): 256-266.
  • Pendharkar, P.C. and Troutt, M.D., 2014. “Interactive classification using data envelopment analysis”, Annals of Operations Research, 214 (1): 125-141.
  • Rubin, A., 1990. “A comparison of linear programming and parametric approaches to the two-group discriminant problem”, Decision Sciences, 21: 373-386.
  • Sueyoshi, T., 1999. “DEA-Discriminant analysis in the view of goal programming”, European Journal of Operational Research, 115: 564-582.
  • Sueyoshi, T., 2001. “Extended DEA-Discriminant analysis”, European Journal of Operational Research, 131: 324-351.
  • Sueyoshi, T., 2004. “Mixed integer programming approach of extended DEA-Discriminant analysis”, European Journal of Operational Research, 152: 45-55.
  • Stam, A., Jones, D.G., 1990. “Classification performance of mathematical programming techniques in discriminant analysis: Results for small and medium sample sizes”, Managerial and Decision Economics, 11: 243-253.
  • Stam, A., Ragsdale, C.T., 1992. “On the classification gap in mathematical programming based approaches to the discriminant problem”, Naval Research Logistic, 39: 545-559.
There are 35 citations in total.

Details

Primary Language Turkish
Journal Section Makaleler
Authors

H. Hasan Örkcü

Mustafa İsa Doğan

Publication Date December 3, 2015
Published in Issue Year 2015 Volume: 19 Issue: 3

Cite

APA Örkcü, H. H., & Doğan, M. İ. (2015). Veri Zarflama Analizi Tabanlı Yeni Bir Hibrid İki Gruplu Sınıflandırma Modeli. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 19(3). https://doi.org/10.19113/sdufbed.13457
AMA Örkcü HH, Doğan Mİ. Veri Zarflama Analizi Tabanlı Yeni Bir Hibrid İki Gruplu Sınıflandırma Modeli. J. Nat. Appl. Sci. December 2015;19(3). doi:10.19113/sdufbed.13457
Chicago Örkcü, H. Hasan, and Mustafa İsa Doğan. “Veri Zarflama Analizi Tabanlı Yeni Bir Hibrid İki Gruplu Sınıflandırma Modeli”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 19, no. 3 (December 2015). https://doi.org/10.19113/sdufbed.13457.
EndNote Örkcü HH, Doğan Mİ (December 1, 2015) Veri Zarflama Analizi Tabanlı Yeni Bir Hibrid İki Gruplu Sınıflandırma Modeli. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 19 3
IEEE H. H. Örkcü and M. İ. Doğan, “Veri Zarflama Analizi Tabanlı Yeni Bir Hibrid İki Gruplu Sınıflandırma Modeli”, J. Nat. Appl. Sci., vol. 19, no. 3, 2015, doi: 10.19113/sdufbed.13457.
ISNAD Örkcü, H. Hasan - Doğan, Mustafa İsa. “Veri Zarflama Analizi Tabanlı Yeni Bir Hibrid İki Gruplu Sınıflandırma Modeli”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 19/3 (December 2015). https://doi.org/10.19113/sdufbed.13457.
JAMA Örkcü HH, Doğan Mİ. Veri Zarflama Analizi Tabanlı Yeni Bir Hibrid İki Gruplu Sınıflandırma Modeli. J. Nat. Appl. Sci. 2015;19. doi:10.19113/sdufbed.13457.
MLA Örkcü, H. Hasan and Mustafa İsa Doğan. “Veri Zarflama Analizi Tabanlı Yeni Bir Hibrid İki Gruplu Sınıflandırma Modeli”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 19, no. 3, 2015, doi:10.19113/sdufbed.13457.
Vancouver Örkcü HH, Doğan Mİ. Veri Zarflama Analizi Tabanlı Yeni Bir Hibrid İki Gruplu Sınıflandırma Modeli. J. Nat. Appl. Sci. 2015;19(3).

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Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi
https://doi.org/10.53092/duiibfd.1309871

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