한국경영과학회지 (Journal of the Korean Operations Research and Management Science Society)

  • 제34권3호
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  • Pages.29-41
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  • 2009
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  • 1225-1119(pISSN)
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  • 2733-4759(eISSN)
한국경영과학회 (The Korean Operations Research and Management Science Society)
권호 표지

유전 알고리듬 기반 제품구매예측 모형의 개발

A GA-based Classification Model for Predicting Consumer Choice

  • 민재형 (서강대학교 경영전문대학원) ;
  • 정철우 (서강대학교 대학원 경영학과)
  • 발행 : 2009.09.30
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⟨ 이전 논문 다음 논문 ⟩

초록

The purpose of this paper is to develop a new classification method for predicting consumer choice based on genetic algorithm, and to validate Its prediction power over existing methods. To serve this purpose, we propose a hybrid model, and discuss Its methodological characteristics in comparison with other existing classification methods. Also, we conduct a series of experiments employing survey data of consumer choices of MP3 players to assess the prediction power of the model. The results show that the suggested model in this paper is statistically superior to the existing methods such as logistic regression model, artificial neural network model and decision tree model in terms of prediction accuracy. The model is also shown to have an advantage of providing several strategic information of practical use for consumer choice.

키워드

참고문헌

  1. 송수섭, 이용훈, '인공신경망을 이용한 소비자 선택 예측에 관한 연구', '한국경영과학회지', 제26권, 제4호(2001), pp.55-70
  2. 안현철, 김경재, 한인구, 'Support Vector Machine을 이용한 고객구매예측모형', '한국지능 정보시스템학회논문지', 제11권, 제3호(2005), pp.69-81
  3. 한상만, 박승배, 정남호, '인공신경망과 로짓모형을 이용한 내구재의 구매의도 예측에 관한 비교연구-대형 TV 시장을 중심으로', '마케팅 연구', 제15권, 제3호(2000), pp.71-92
  4. Agrawal, D., C. Schorling, 'Market share forecasting: An empricial comparison of artificial neural networks and multinomial logit model,' Journal of Retailing, Vol.72, No.4(1996), pp.383-407 https://doi.org/10.1016/S0022-4359(96)90020-2
  5. Ahn, H, K. Kim, I. Han, 'Hybrid genetic algorithms and case-based reasoning systems for customer classification,' Expert Systems, Vol.23, No.3(2006), pp.127-144 https://doi.org/10.1111/j.1468-0394.2006.00329.x
  6. Ahn, H, K. Kim, I. Han, 'A case-based reasoning system with the two-dimensional reduction technique for customer classification,' Expert Systems with Applications, Vol.32, No.4(2007), pp.1011-1019 https://doi.org/10.1016/j.eswa.2006.02.021
  7. Allenby, G.M, T.S. Shively, S. Yang, M.J. Garratt, 'A choice model for packaged goods : Dealing with discrete quantities and quantity discounts source,' Marketing Science, Vol.23, No.1(2004), pp.95-108 https://doi.org/10.1287/mksc.1030.0022
  8. Athanassopoulos, A.D., S.P. Curram, 'A Comparison of data envelopment analysis and artificial neural networks as tools for assessing the efficiency of decision making units,' Journal of the Operational Research Society, Vol.47, No.8(1996), pp.1000-1016 https://doi.org/10.1057/jors.1996.127
  9. Ben-Akiva, M., B. Boccara, 'Discrete choice models with latent choice sets,' International Journal of Research in Marketing, Vol.l2, No.1(1995) , pp.9-24 https://doi.org/10.1016/0167-8116(95)00002-J
  10. Bolduc, D., 'A practical technique to estimate multinomial probit models in transportation,' Transportation Research B, Vol.33, No.1(1999), pp.63-79 https://doi.org/10.1016/S0191-2615(98)00028-9
  11. Bolduc, D, B. Fortin, S. Gordon, 'Multinomial probit estimation of spatially interdependent choices : An empirical comparison of two new techniques,' International Regional Science Review, Vol.20, No.1-2(1997), pp.77-101 https://doi.org/10.1177/016001769702000105
  12. Borsch-Supan, A, V. Hajivassiliou, 'Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models,' Journal of Econometrics, Vol.58, No.3(1993), pp.347-368 https://doi.org/10.1016/0304-4076(93)90049-B
  13. Brownstone, D, D.S. Bunch, K. Train, 'Joint mixed logit models of stated and revealed preferences for alternative fuel vehicles,' Transportation Research B, Vol.34, No.5(2000), pp.315-338 https://doi.org/10.1016/S0191-2615(99)00031-4
  14. Chiu, C., 'A case-based customer classification approach for direct marketing,' Expert Systems with Applications, Vol.22, No.2(2002), pp.163-168 https://doi.org/10.1016/S0957-4174(01)00052-5
  15. Curram, S., J. Mlingers, 'Neural networks, decision tree induction and discriminant analysis: an empirical comparison,' Journal of the Operational Research Society, Vol.45, No.4 (1994), pp.440-450 https://doi.org/10.1057/jors.1994.62
  16. Gilbride, T.J., G.M. Allenby, 'A choice model with conjunctive, disjunctive, and compensatory screening rules,' Marketing Science, Vol.23,No.3(2004), pp.391-406 https://doi.org/10.1287/mksc.1030.0032
  17. Guadagni, P.M, J,D.C. Little, 'A logit model of brand choice calibrated on scanner data,' Marketing Science, Vol.2, No.3(1983), pp.203-238 https://doi.org/10.1287/mksc.2.3.203
  18. Ha, K., S. Cho, D. MacLachlan, 'Response models based on bagging neural networks,' Journal of Interactive Marketing, Vol.19, No.1 (2005), pp.17-30 https://doi.org/10.1002/dir.20028
  19. Holland, J,H., Adaptation in natural and artificial systems, The University of Michigan Press, Ann Arbor, MI, 1975
  20. Keane, M, 'A computationally practical simulation estimator for panel data,' Econometrica, Vol.62, No.1(1994) , pp.95-116 https://doi.org/10.2307/2951477
  21. Kecman, V., Learning and Soft Computing, MIT Press, Cambridge, Mass, 2001
  22. Kumar, A, Y.R. Rao, H. Soni, 'An empirical comparison of neural network and logistic regression models,' Marketing Letters, Vol.6, No.4(1995), pp.251-263 https://doi.org/10.1007/BF00996189
  23. Lachaab, M, A. Ansari, K. Jedidi, A. Trabelsi, 'Modeling preference evolution in discrete choice models : A Bayesian state-space approach,' Quantitative Marketing and Economics, Vol.4, No.1 (2006), pp.57-81 https://doi.org/10.1007/s11129-006-6559-x
  24. McCulloch, R., N. Polson, P.E. Rossi, 'A Bayesian analysis of the multinomial probit model with fully identified parameters,' Journal of Econometrics, Vol.99, No.1(2000), pp.173-193 https://doi.org/10.1016/S0304-4076(00)00034-8
  25. McFadden, D., 'A method of simulated moments for the estimation of discrete response models without numerical integration,' Econometrica, Vol.57, No.5(1989), pp.995-1026 https://doi.org/10.2307/1913621
  26. McFadden, D., K Train, 'Mixed MNL models for discrete response,' Journal of Applied Econometrics, Vol.15, No.5(2000), pp.447-470 https://doi.org/10.1002/1099-1255(200009/10)15:5<447::AID-JAE570>3.0.CO;2-1
  27. Paap, R., P.H. Franses, 'A dynamic multinomial probit model for brand choice with different long-run and short-run effects of marketing-mix variables,' Journal of Applied Econometrics, Vol.15, No.6, Special Issue : Inference and Decision Making(2000), pp. 717-744 https://doi.org/10.1002/jae.580
  28. Rossi, P.E., G.M. Allenby, 'Bayesian statistics and marketing,' Marketing Science, Vol.22, No.3(2003), pp.304-328 https://doi.org/10.1287/mksc.22.3.304.17739
  29. Venugopal, V., W. Baets, 'Neural networks and statistical techniques in marketing research : a conceptual comparison,' Marketing Intelligence and Planning, Vol.12, No.7(1994), pp.30-38 https://doi.org/10.1108/02634509410065555
  30. West, P.M, P.L. Brockett, L.L. Golden, 'A comparative analysis of neural networks and statistical methods for predicting consumer choice,' Marketing Science, Vol.16, No.4(1997), pp.370-391 https://doi.org/10.1287/mksc.16.4.370
  31. van Wezel, M.C., W.R.J. Baets, 'Predicting market responses with a neural network : the case of fast moving consumer goods,' Marketing Intelligence and Planning, Vol.13, No.7(1995), pp.23-30 https://doi.org/10.1108/02634509510093797