Xiao-Yong Lu
ABSTRACT
To predict the preference of customer is essential to every financial service company, and by developing a high-efficient classification model can make a company to increase their profit and reduce the cost. In marketing, the uses of big data include "recommendation engines" to make suggestions based on the prior interests of a customer as compared to others. Thus, the main idea of this research presents an artificial immune classification combining collaborative filtering approach for bank term deposit recommendation. Artificial Immune Network (AIN) is a network of customers with bank term deposit and it can be adopted as a group decision making model in predicting whether a new customer will have a term deposit or not. A series of experiments are conducted, and the results are very encouraging. In spite of the class imbalance problem in the test dataset, our proposed model outperformed other models with highest accuracy.
- Hu, X. H. 2005. a data mining approach for retailing bankcustomer attrition analysis. Applied Intelligence. 22, 1, 47--60. Google Scholar
Digital Library
- Nasir, A. N. M., Selamat, A. and Selamat, H. 2009. An artificial immune system for recommending relevant information through political weblog. Proceedings of iiWAS2009. 420--424. Google ScholarDigital Library
- Dudek, G. 2012. An artificial immune system for classification with local feature selection. IEEE Transaction on Evolutionary Computation. 16, 6, 847--860. Google ScholarDigital Library
- Jerne, N. K. 1974. Towards a network theory of the immune system. Collect. Ann. Inst. Pasteur. 125C, 1-2, 373--389.Google Scholar
- Aydin, I., Karakose, M., and Akin, E. 2012. An adaptive artificial immune system for fault classification. Journal of Intelligent Manufacturing. 23, 5, 1489--1499. Google ScholarDigital Library
- Chen, M. H., Chang, P. C., and Lin, C. H. 2013. A self-evolving artificial immune system II with T-cell and B- cell for permutation flow-shop problem. Springer Science & Business Media New York. 25, 6, 1257--1270. Google ScholarDigital Library
- Alatas, B. and Akin, E. 2005. Mining fuzzy classification rules using an artificial immune system with boosting. Lecture Notes in Computer Science. 3631, 283--293. Google ScholarDigital Library
- Sengur, A. 2009. Prediction of protein cellular localization sites using a hybrid method based on artificial immune system and fuzzy k-NN algorithm. Digital Signal Processing. 19, 5, 815--826. Google ScholarDigital Library
- Knight, T. and Timmis, J. 2001. AINE: An immunological approach to data mining. Proceedings 2001 IEEE International Conference on Data Mining. 69, 297--304. Google ScholarDigital Library
- Paul T., Polack F. A. C. and Timmis, J. 2013. Accelerating Immunos 99. Artificial Immune System -- ICARIS. 893--898.Google Scholar
- Liu, H. F., Hu, Z., Mian, A., Tian, H. and Zhu, X. Z. 2014. A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based Systems. 56, 156--166. Google ScholarDigital Library
- Su, P. and Ye, H. 2009. An item based collaborative filtering recommendation algorithm using rough set prediction. International Joint Conference on Artificial Intelligence. 308--311. Google ScholarDigital Library
- Gong, S., Ye, H. and Tan, H. 2009. Combining memory-based and model-based collaborative filtering in recommender system. Pacific-Asia Conference on Circuits, Communications and System. 690--693. Google ScholarDigital Library
- Ramezani, M., Moradi, P. and Akhlaghian, F. 2014. A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains. Statistical Mechanics and its Applications. 408, 72--84.Google Scholar
- Moro, S., Cortez, P. and Rita, P. 2014. A data-driven approach to predict the success of bank telemarketing. Decision Support Systems. 62, 22--31.Google ScholarCross Ref
- Gadi, M. F. A., Wang, X. D., and Lago, A. P. D. 2008. Credit card fraud detection with artificial immune system. Artificial Immune Systems. 5132, 119--131. Google ScholarDigital Library
- Nie, G., Rowe, W., Zhang, L., Tian, Y. G. and Shi, Y. 2011. Credit card churn forecasting by logistic regression and decision tree. Expert System with Applications. 38, 12, 15273--15285. Google ScholarDigital Library
- Chen, Z. Y., Fan, Z. P. and Sun, M. 2012. A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data. European Journal of operational research. 223, 2, 461--472.Google Scholar
- Farquad, M. A. H., Ravi, V. and Raju, S. B. 2014. Churn prediction using comprehensible support vector machine: an analytical CRM application. Applied Soft Computing. 19, 31--40.Google ScholarCross Ref
- Stehman, Stephen V. 1997. Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment. 62, 1, 77--89.Google ScholarCross Ref
Index Terms
- Data Analytics for Bank Term Deposit by Combining Artificial Immune Network and Collaborative Filtering
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