基于半监督学习的朴素贝叶斯分类新算法

doi:10.13229/j.cnki.jdxbgxb201603031

吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (3): 884-889.doi: 10.13229/j.cnki.jdxbgxb201603031

基于半监督学习的朴素贝叶斯分类新算法

董立岩¹, 隋鹏¹, 孙鹏¹, 李永丽²

1.吉林大学计算机科学与技术学院, 长春 130012;
2.东北师范大学计算机科学与信息技术学院,长春 130117

收稿日期:2013-08-01 出版日期:2016-06-20 发布日期:2016-06-20
作者简介:董立岩(1966),男,教授,博士生导师.研究方向:数据挖掘.E-mail:dongly@jlu.edu.cn
基金资助:
国家自然科学基金项目(61272209).

Novel naive Bayes classification algorithm based on semi-supervised learning

DONG Li-yan¹, SUI Peng¹, SUN Peng¹, LI Yong-li²

1.College of Computer Science and Technology , Jilin University , Changchun 130012, China;
2.School of Computer Science and Technology, Northeast Normal University, Changchun 130117,China

Received:2013-08-01 Online:2016-06-20 Published:2016-06-20

摘要/Abstract

摘要： 为了在有标签的训练集中保留高质量的样本,首先利用无标签训练集得出置信度高的k个样本,再结合有标签训练样本,不断迭代直至训练完成。实验结果表明:随着无标记样本比例的不断增加,本文算法预测准确性明显高于朴素贝叶斯分类算法,而且其性能比传统半监督学习方法有所改善。

关键词: 计算机应用, 半监督学习, 朴素贝叶斯, 无类标签分类

Abstract: A novel naive Bayes classification algorithm based on semi-supervised learning is proposed. First, to retain high quality samples in the training sect with class label, the unlabeled training set is sued to obtain k samples of high confidence. Then, these high confidence samples are combined with the labeled-training samples to iterate until the training is complete. The experimental results show that with the increasing proportion of unlabeled samples, the predictive accuracy of the proposed algorithm is significant higher than that of the Na?ve Bayesian classification. In addition, the effectiveness and performance of the algorithm are improved compared with the traditional semi-supervised learning algorithm.

Key words: computer application, semi-supervised study, Naive Bayes, unknown label classification

中图分类号:

TP301.6

董立岩, 隋鹏, 孙鹏, 李永丽. 基于半监督学习的朴素贝叶斯分类新算法[J]. 吉林大学学报(工学版), 2016, 46(3): 884-889.

DONG Li-yan, SUI Peng, SUN Peng, LI Yong-li. Novel naive Bayes classification algorithm based on semi-supervised learning[J]. 吉林大学学报(工学版), 2016, 46(3): 884-889.

参考文献

[1] Zhang H, Sheng S. Learning weighted naive Bayes with accurate ranking[C]∥Proceedings of the Fourth IEEE International Conference on Data Mining, Brighton, 2004: 567-570.
[2] Zheng Z, Webb G I. Lazy learning of bayesian rules[J]. Machine Learning, 2000,41(1):53-84.
[3] Wang Z H, Webb G I,Zheng F. Adjusting Dependence Relations for Semi-lazy TAN Classfier[M]. Berlin:Springer,2003:453-456.
[4] Yager R R. An extension of the naive Bayesian classifier[J]. Information Sciences, 2006,176(5): 577-588.
[5] 江凯,高阳. 并行化的半监督朴素贝叶斯分类算法[J]. 计算机科学与探索,2012, 6(10):912-918.
Jiang Kai,Gao Yang. A parallelized semi-supervised na?ve bayes classifier[J]. Journal of Frontiers of Computer Science and Technology,2012, 6(10):912-918.
[6] Shahshahani B M, Landgrebe D A. The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon[J].IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(5): 1087-1095.
[7] Kulis B, Basu S, Dhillon I, et al. Semi-supervised graph clustering: a kernel approach[J]. Machine learning, 2009, 74(1): 1-22.
[8] 彭兴媛. 朴素贝叶斯分类改进算法的研究[D].重庆:重庆大学数学与统计学院,2012.
Peng Xin-yuan. Research on naive Bayesian classifier algorithm[D].Chongqing: School of Mathematics and Statistics, Chongqing University,2012.
[9] Su J, Shirab J S, Matwin S. Large scale text classification using semi-supervised multinomial naive bayes[C]∥Proceedings of the 28th International Conference on Machine Learning,Bellevue, WA, USA,2011: 97-104.
[10] 孔怡青. 半监督学习及其应用研究[D]. 无锡:江南大学信息工程学院,2009.
Kong Yi-qing. Studies on semi-supervised learning and its applications[D]. Wuxi: School of Information Engineering, Jiangnan University,2009.
[11] Mann G S, McCallum A. Generalized expectation criteria for semi-supervised learning with weakly labeled data[J]. The Journal of Machine Learning Research, 2010, 11: 955-984.
[12] Hall M, Frank E, Holmes G, et al. The WEKA data mining software: an update[J]. ACM SIGKDD Explorations Newsletter,2009,11(1): 10-18.
[13] Zhu X J,Goldberg A B.Introduction to semi-supervised learning[J].Synthesis Lectures on Artificial Intelligence and Machine Learning,2009,3(1):1-130.
[14] Kveton B, Valko M, Rahimi A, et al. Semi-supervised learning with max-margin graph cuts[C]∥Thirteenth International Conference on Artificial Intelligence and Statistics, Sardinia, Italy, 2010:421-428.
[15] UCI machine learning repository[DB/OL]. [2013-05-20].http://archive.ics.uci.edu/ml/index.html
[16] Modha D S, Spangler W S. Feature weighting in k-means clustering[J]. Machine Learning, 2003, 52(3): 217-237.

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基于半监督学习的朴素贝叶斯分类新算法

Novel naive Bayes classification algorithm based on semi-supervised learning

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