全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

扩展的树增强朴素贝叶斯分类器*

, PP. 469-474

Keywords: 朴素贝叶斯分类器,学习贝叶斯网,树增强朴素贝叶斯分类器(TAN),扩展的树增强朴素贝叶斯分类器(ETAN)

Full-Text   Cite this paper   Add to My Lib

Abstract:

树增强朴素贝叶斯分类器继承了朴素贝叶斯分类器计算简单和鲁棒性的特点,同时分类性能常常优于朴素贝叶斯分类器,然而在有连续变量的情况下要求必须进行预离散化.为了更好地表达数据的分布,减少信息损失,有必要考虑混合数据的情况.本文推导混合数据的极大似然函数,提出扩展的树增强朴素贝叶斯分类器,突破必须对连续变量进行预离散化的限制,能够在树增强朴素贝叶斯分类器的框架内处理混合变量的情况.实验测试证明其具有良好的分类精度.

References

[1]  Richard O D, Peter E H, David G S. Pattern Classification. 2nd Edition. New York, USA: John Wiley & Sons, 2001 (Richard O D, Peter E H, David G S,著; 李宏东,等,译.模式分类.第2版.北京:机械工业出版社, 2003)
[2]  Langley P, Iba W, Thompson K. An Analysis of Bayesian Classifiers. In: Proc of the 10th National Conference on Artificial Intelligence. San Jose, USA: AAAI Press, 1992, 223-228
[3]  Friedman N, Geiger D, Goldszmidt M. Bayesian Network Classifiers. Machine Learning, 1997, 29(2-3): 131-163
[4]  Neapolitan R E. Learning Bayesian Networks. Upper Saddle River, USA: Prentice Hall, 2003
[5]  Chickering D M, Geiger D, Heckerman D. Learning Bayesian Networks is NP-Complete. In: Fisher D H, Lenz H J, eds. Learning from Data: Artificial Intelligence and Statistics. New York, USA: Springer-Verlag, 1996, 121-130
[6]  Geiger D. An Entropy-Based Learning Algorithm of Bayesian Conditional Trees. In: Proc of the 8th Annual Conference on Uncertainty in Artificial Intelligence. San Mateo, USA: Morgan Kaufmann, 1992, 92-97
[7]  Chow C K, Liu C N. Approximating Discrete Probability Distributions with Dependence Trees. IEEE Trans on Information Theory, 1968, 14(3): 462-467
[8]  Tarjan R E. Finding Optimal Branchings. Networks, 1977, 7: 25-35
[9]  Johnson R A, Wichern D W. Applied Multivariate Statistical Analysis. 4th Edition. Upper Saddle River, USA: Prentice Hall, 1998 (Johnson R A, Wichern D W,著;陆璇,译. 实用多元统计分析. 北京: 清华大学出版社, 2001)
[10]  Murphy P M, Aha D W. UCI Repository of Machine Learning Database. http://www.ics.uci.edu/~mlearn/MLRepository.html
[11]  Fayyad U M, Irani K B. Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In: Proc of the 13th International Joint Conference on Artificial Intelligence. San Mateo, USA: Morgan Kaufmann, 1993, 1022-1027
[12]  Russell S, Norvig P. Artificial Intelligence: A Modern Approach. 2nd Edition. Upper Saddle River, USA: Prentice Hall, 1998 (Russell S, Norvig P,著;姜 哲,等,译.人工智能——一种现代方法.第2版.北京:人民邮电出版社, 2004)
[13]  Monti S, Cooper G F. Learning Hybrid Bayesian Networks from Data. In: Jordan M I, ed. Learning in Graphical Models. Dordrecht, Netherlands: Kluwer Academic Publishers, 1998, 521-540

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133