Innovation Management in Defensive Organizations

Innovation Management in Defensive Organizations

Provide a dynamic algorithm using meta-heuristic models in data flow based on resampling to improve customer response prediction

Document Type : Original Article

Authors
Associate Professor, Faculty of Management and Accounting, Islamic Azad University, Central Tehran Branch, Tehran, Iran.

بصیری، مهدی، «کاربرد تکنیک داده‌کاوی در مدیریت روابط مشتری»، همایش ملی تجارت الکترونیکی، وزارت صنعت، معدن و تجارت، انجمن علمی تجارت الکترونیکی ایران، 1386، دوره 4.
بون و کورتز (2013)، مدیریت بازاریابی نوین، ترجمه نوروزی، حسین، و مهدی مهذبی (1395)، تهران، انتشارات فوژان.
حافظ‌نیا، محمدرضا (1389)، مقدمه‌ای بر روش تحقیق در علوم انسانی، تهران، انتشارات سمت.
رضائی نوائی، سمیرا، کوشا، حمیدرضا، «به‌کارگیری و ارزیابی تکنیک‌های داده‌کاوی جهت پیش‌بینی رویگردانی مشتری در صنعت بیمه»، نشریة بین المللی مهندسی صنایع و مدیریت تولید، دوره (27), شماره (4), سال (1-2017)، صفحات (635-653).
لینکستر و مسینگهام (2001)، اصول مدیریت بازاریابی، ترجمه نوروزی، حسین، و نیما سلطانی‌نژاد (1395)، تهران، مؤسسه کتاب مهربان نشر.
 
Baesens, B., Viaene, S., Van den Poel, D., Vanthienen, J., & Dedene, G. (2002). Bayesian neural network learning for repeat purchase modelling in direct marketing. European Journal of Operational Research, 138(1), 191-211.
Barandelaa, R., Sanchezb, J., & Garcia, V. (2003). Strategies for learning in class imbalance problems.
Berry, M. J., & Linoff, G. (1997). Data mining techniques: for marketing, sales, and customer support: John Wiley & Sons, Inc.
Binder, D. A. (1983). On the variances of asymptotically normal estimators from complex surveys. International Statistical Review/Revue Internationale de Statistique, 279-292.
Błaszczyński, J., Dembczyński, K., Kotłowski, W., & Pawłowski, M. (2006). Mining direct marketing data by ensembles of weak learners and rough set methods. Paper presented at the International Conference on Data Warehousing and Knowledge Discovery.
Buhrman, H., & De Wolf, R. (2002). Complexity measures and decision tree complexity: a survey. Theoretical Computer Science, 288(1), 21-43.
Chawla, N. V. (2009). Data mining for imbalanced datasets: An overview Data mining and knowledge discovery handbook (pp. 875-886): Springer.
Chen, Z.-Y., Fan, Z.-P., & Sun, M. (2015). Behavior-aware user response modeling in social media: Learning from diverse heterogeneous data. European Journal of Operational Research, 241(2), 422-434.
Coenen, F., Swinnen, G., Vanhoof, K., & Wets, G. (2000). The improvement of response modeling: combining rule-induction and case-based reasoning. Expert Systems with Applications, 18(4), 307-313.
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2012). A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 463-484.
Gönül, F. F., & Hofstede, F. T. (2006). How to compute optimal catalog mailing decisions. Marketing Science, 25(1), 65-74.
Hill, S., Provost, F., & Volinsky, C. (2006). Network-based marketing: Identifying likely adopters via consumer networks. Statistical Science, 21(2), 256-276.
Judd, J. S. (1990). Neural network design and the complexity of learning: MIT press.
Kang, P., Cho, S., & MacLachlan, D. L. (2012). Improved response modeling based on clustering, under-sampling, and ensemble. Expert Systems with Applications, 39(8), 6738-6753.
Knott, A., Hayes, A., & Neslin, S. A. (2002). Next‐product‐to‐buy models for cross‐selling applications. Journal of interactive Marketing, 16(3), 59-75.
Lai, Y.-T., Wang, K., Ling, D., Shi, H., & Zhang, J. (2006). Direct marketing when there zre voluntary buyers. Paper presented at the Data Mining, 2006. ICDM'06. Sixth International Conference on.
Li, D.-C., Liu, C.-W., & Hu, S. C. (2010). A learning method for the class imbalance problem with medical data sets. Computers in biology and medicine, 40(5), 509-518.
Ling, C. X., & Li, C. (1998). Data mining for direct marketing: Problems and solutions. Paper presented at the Kdd.
Linoff, G. S., & Berry, M. J. (2011). Data mining techniques: for marketing, sales, and customer relationship management: John Wiley & Sons.
Moro, S., Cortez, P., & Rita, P. (2014). A data-driven approach to predict the success of bank telemarketing. Decision Support Systems, 62, 22-31.
Moro, S., Laureano, R., & Cortez, P. (2011). Using data mining for bank direct marketing: An application of the crisp-dm methodology. Paper presented at the Proceedings of European Simulation and Modelling Conference-ESM'2011.
Napierała, K., Stefanowski, J., & Wilk, S. (2010). Learning from imbalanced data in presence of noisy and borderline examples. Paper presented at the International Conference on Rough Sets and Current Trends in Computing.
Sun, B., Li, S., & Zhou, C. (2006). “Adaptive” learning and “proactive” customer relationship management. Journal of Interactive Marketing, 20(3-4), 82-96.
Sun, Y., Wong, A. K., & Kamel, M. S. (2009). Classification of imbalanced data: A review. International Journal of Pattern Recognition and Artificial Intelligence, 23(04), 687-719.
Wilkinson, T. J., McAlister, A., & Widmier, S. (2007). Reaching the international consumer: An assessment of the international direct marketing environment. Direct Marketing: An International Journal, 1(1), 17-37.
Yan, R., Liu, Y., Jin, R., & Hauptmann, A. (2003). On predicting rare classes with SVM ensembles in scene classification. Paper presented at the Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP'03). 2003 IEEE International Conference on.
Yang, Q., & Wu, X. (2006). 10 challenging problems in data mining research. International Journal of Information Technology & Decision Making, 5(04), 597-604.
Zhang, S., Liu, L., Zhu, X., & Zhang, C. (2008). A strategy for attributes selection in cost-sensitive decision trees induction. Paper presented at the Computer and Information Technology Workshops, 2008. CIT Workshops 2008. IEEE 8th International Conference on.