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基于Boosting的市场值函数算法及其评价

Keywords: Boosting算法,市场值函数,lift值

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Abstract:

发现具有潜在市场价值的客户群是直销中的一个关键问题,尽管一些标准的数据挖掘算法可以用来解决此问题,但效果并不理想.为此,采用市场值函数算法,它以信息论为基础,通过构造一个线性市场值函数来对客户进行排序,从而发现最具有潜在市场价值的客户群.实验结果表明,它的评价值可达80%以上,并且具有很好的可解释性.同时,将Boosting算法应用到市场值函数算法中,用以提高市场值函数的预测效果;在3个不同的数据集上进行了实验,评价值均提高了一个百分点.

References

[1]  TOM M M. Machine Learning[M]. New York: The McGraw-Hill Company, Inc, 1997.
[2]  YAO Y Y, ZHONG N. Mining market value function for targeted marketing[A]. 25th Annual International Computer Software and Applications Conference (COMPSAC' 01) [C]. Chicago: IEEE Computer Society Press,2001. 517-522.
[3]  黄佳进,刘椿年,李文斌.市场值函数挖掘的研究和实现[J].北京工业大学学报,2003,29(1):94-97.
[4]  HUANG Jia-jin, LIU Chun-nian, LI Wen-bin. Research and implemenitation of mining marker value function[J].
[5]  Journal of Beijing University of Technology, 2003, 29(1): 94-97. (in Chinese)
[6]  ROBERT E S, YORAM S. Improved boosting algorithms using confidence-related predictions[J]. Machine Learning,1999, 37(3): 297-336.
[7]  ELKAN C. Boosting and Naive Bayesian Learning[R]. San Diego: University of California, 1997.
[8]  DRUCKER H, CORTES C. Boosting decision trees[J]. Advances in Neural Information Processing Systems, 1996,8: 479-485.
[9]  LING C X, Li C H. Data mining for direct marketing: Problems and solutions[A]. Proceeding 4th International
[10]  Conference on Knowledge Discovery and Data Mining[C]. New York: AAAI press, 1996. 73-79.
[11]  KAYMAK U, SETNES M. Target Selection Based on Fuzzy Clustering: A Volume Prototype Approach to Coil
[12]  Challenge 2000[R]. Leiden: Amsterdam and Leiden Institute of Advanced Computer Science, 2000. 1-6.

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