Consumption Pattern Recognition System Based on SVM

摘要：

In this paper, we present a consumption pattern recognition system based on SVM. It can produce an optimized classification pattern using SVM algorithm and use the pattern to predict consumer behaviors. In this system, three dimension reduction methods including Principal Component Analysis (PCA), correlation analysis and data cubes are applied to reduce dimension of features and two training methods including Support Vector Machine (SVM) and Support Vector Machine by Increasing Negative Examples (SVMINE) are utilized to build classifiers. Consumption pattern recognition system can find the consumption habits of specific consumer group which are helpful to well-targeted marketing. Empirical results show that the system can recognize different consumption pattern with high efficiency and accuracy.

关键词： pattern recognition SVM dimension reduction classification

作者: Dazhen Huang Zhihua Huang

作者单位: College of Mathematics and Computer Science Fuzhou University Fuzhou 350108, China

会议类型: 国际会议

会议名称: 2011 Fourth International Conference on Intelligent Computation Technology and Automation(2011年第四届智能计算技术与自动化国际会议 ICICTA 2011)

会议地点: 深圳

会议语种:英文

页码: 79-82

在线出版日期: 2011-03-28（万方平台首次上网日期，不代表论文的发表时间）