Learning and optimization of dynamic naive Bayesian classifiers for small time series

被引：0
|
作者：
Wang S.-C. [1 ,2 ]
Gao R. [1 ]
Du R.-J. [1 ]
机构：
[1] School of Mathematics & Information, Shanghai Lixin University of Commerce, Shanghai
[2] Lixin Accounting Research Institute, Shanghai Lixin University of Commerce, Shanghai
来源：
Wang, Shuang-Cheng (wangsc@lixin.edu.cn) | 1600年 / Northeast University卷 / 32期
关键词：
Bayesian network; Classifiers; Gaussian kernel function; Smoothing parameter; Time series;
D O I：
10.13195/j.kzyjc.2015.1556
中图分类号：
学科分类号：
摘要：
The small time series exists generally in the field of macroeconomy. There are wide demands for the classification of small time series in macroeconomy. Because the information contained in the small time series is not sufficient, it is very difficult to effectively improve the reliability of small time series classification. In view of this situation, on the basis of using the Gaussian kernel function of introducing the smoothing parameter to estimate the attribute marginal density, the dynamic naive Bayesian classifier for small time series classification is presented, and the synchronous and asynchronous optimization method for smoothing parameters are given. The experimental results show that the classification accuracy of the small time series classifier can be improved significantly by optimization. © 2017, Editorial Office of Control and Decision. All right reserved.
引用
收藏
页码：163 / 166
页数：3
相关论文
共 10 条
  • [1] Friedman N., Murphy K., Russell S., Learning the structure of dynamic probabilistic networks, Proc of the 14th Int Conf on Uncertainty in Artificial Intelligence, pp. 139-147, (1998)
  • [2] Gao R., Wang S.C., Du R.J., Extending dynamic naive Bayesian classifier for the time-delay impact analysis of macroeconomic boom index, ICIC Express Letters. Part B, Applications: An International J of Research and Surveys, 6, 11, pp. 3127-3133, (2015)
  • [3] Friedman N., Geiger D., Goldszmidt M., Bayesian network classifiers, Machine Learning, 29, 2-3, pp. 131-161, (1997)
  • [4] John G.H., Langley P., Estimating continuous distributions in Bayesian classifiers, Proc of the Eleventh Conf on Uncertainty in Artificial Intelligence(UAI-1995), pp. 338-345, (1995)
  • [5] Perez A., Larranagaa P., Inzaa I., Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes, Int J of Approximate Reasoning, 43, 1, pp. 1-25, (2006)
  • [6] Perez A., Larranga P., Inza I., Bayesian classifiers based on kernel density estimation: Flexible classifiers, Int J of Approximate Reasoning, 50, 2, pp. 341-362, (2009)
  • [7] Wang S.C., Gao R., Du R.J., Restricted Gaussian classification network, Acta Automatica Sinica, 41, 12, pp. 2128-2140, (2015)
  • [8] Wang S.C., Gao R., Du R.J., Dependency extension of naive Bayesian classifiers based on Gaussian kernel function, Control and Decision, 30, 12, pp. 2280-2284, (2015)
  • [9] Dong W.Y., Zhou M.C., Gaussian classifier-based evolutionary strategy for multimodal optimization, IEEE Trans on Neural Networks & Learning Systems, 25, 6, pp. 1200-1216, (2014)
  • [10] Pavani S.K., Gomez D.D., Frangi A.F., Gaussian weak classifiers based on co-occurring Haar-like features for face detection, Pattern Analysis and Applications, 17, 2, pp. 431-439, (2014)
1