论文信息 - Consumption Pattern Recognition System Based on SVM

Consumption Pattern Recognition System Based on SVM

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 (SVM-INE) 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.

Zhihua Huang | Dazhen Huang

[1] Xin Li,et al. Protein classification with imbalanced data , 2007, Proteins.

[2] Mu-Chen Chen,et al. Credit scoring with a data mining approach based on support vector machines , 2007, Expert Syst. Appl..

[3] Francis Eng Hock Tay,et al. Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

[4] Chih-Jen Lin,et al. Manuscript Number: 2187 Training ν-Support Vector Classifiers: Theory and Algorithms , 2022 .

[5] Chih-Jen Lin,et al. A Simple Decomposition Method for Support Vector Machines , 2002, Machine Learning.

[6] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[7] Zhen-Xing Zhang,et al. Comparing the Feature Selection Using the Distributed Non-overlap Area Measurement Method with Principal Component Analysis , 2009, 2009 Eighth IEEE/ACIS International Conference on Computer and Information Science.

[8] Chih-Jen Lin,et al. Training v-Support Vector Classifiers: Theory and Algorithms , 2001, Neural Computation.

[9] Young-Chan Lee,et al. Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters , 2005, Expert Syst. Appl..