改进的朴素贝叶斯增量算法研究

曾谁飞; 张笑燕; 杜晓峰; 陆天波

doi:10.11959/j.issn.1000-436x.2016199

您当前的位置：

首页 >

文章列表页 >

改进的朴素贝叶斯增量算法研究

学术论文 | 更新时间：2024-06-05

- 改进的朴素贝叶斯增量算法研究
- Improved incremental algorithm of Naive Bayes
- 通信学报 2016年37卷第10期页码：81-91
- 作者机构：
  
  1. 北京邮电大学软件学院，北京 100876
  2. 北京邮电大学计算机学院，北京 100876
- 作者简介：
  
  [ "曾谁飞（1978-），男，江西广昌人，北京邮电大学博士生，主要研究方向为智能信息处理、机器学习、深度学习和神经网络等。" ]
  [ "张笑燕（1973-），女，山东烟台人，博士，北京邮电大学教授，主要研究方向为软件工程理论、移动互联网软件、ad hoc和无线传感器网络。" ]
  [ "杜晓峰（1973-），男，陕西韩城人，北京邮电大学讲师，主要研究方向为云计算与大数据分析。" ]
  [ "陆天波（1977-），男，贵州毕节人，博士，北京邮电大学副教授，主要研究方向为网络与信息安全、安全软件工程和P2P计算。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-436x.2016199
  中图分类号： TP181
- 网络出版日期：2016-10，
  
  纸质出版日期：2016-10-25
- 稿件说明：
移动端阅览
曾谁飞, 张笑燕, 杜晓峰, 等. 改进的朴素贝叶斯增量算法研究[J]. 通信学报, 2016,37(10):81-91.

Shui-fei ZENG, Xiao-yan ZHANG, Xiao-feng DU, et al. Improved incremental algorithm of Naive Bayes[J]. Journal on communications, 2016, 37(10): 81-91.
曾谁飞, 张笑燕, 杜晓峰, 等. 改进的朴素贝叶斯增量算法研究[J]. 通信学报, 2016,37(10):81-91. DOI： 10.11959/j.issn.1000-436x.2016199.

Shui-fei ZENG, Xiao-yan ZHANG, Xiao-feng DU, et al. Improved incremental algorithm of Naive Bayes[J]. Journal on communications, 2016, 37(10): 81-91. DOI： 10.11959/j.issn.1000-436x.2016199.

摘要

提出了一种新增特征的朴素贝叶斯增量算法。在无标注语料增量样本的选择上，借助传统的类置信度阈值，构建一个最小后验概率作为样本选择的双阈值，当识别到增量语料中有新的特征时，会将该特征加入到特征空间，并对分类器进行相应的更新，发现对类置信度阈值起到很好的补充作用，最后利用了无标注和有标注语料验证所提算法。实验结果表明，改进的朴素贝叶斯增量算法较传统增量算法表现出了更优的增量学习效果。

Abstract

A novel Naive Bayes incremental algorithm was proposed

which could select new features.For the incremental sample selection of the unlabeled corpus

a minimum posterior probability was designed as the double threshold of sample selection by using the traditional class confidence.When new feature was detected in the corpus

it would be mapped into feature space

and then the corresponding classifier was updated.Thus this method played a very important role in class confidence threshold.Finally

it took advantage of the unlabeled and annotated corpus to validate improved incremental algorithm of Naive Bayes.The experimental results show that an improved incremental algorithm of Naive Bayes significantly outperforms traditonal incremental algorithm.

关键词

Keywords

references

CHRISTOPHER M . Pattern recognition and machine learning [M ] . New York : SpringerPress , 2006 .

ESCALANTE H J , MORALES E F , SUCAR L E . A Naive Bayes baseline for early gesture recognition [J ] . Pattern Recognition Letters , 2016 , 73 : 91 - 99 .

DIMKOVSKI M , AN A . A Bayesian model for canonical circuits in the neocortex for parallelized and incremental learning of symbol representations [J ] . Neurocomputing , 2015 , 149 : 1270 - 1279 .

FENG L , WANG Y , ZUO W . Quick online spam classification method based on active and incremental learning [J ] . Journal of Intelligent ＆Fuzzy Systems , 2015 , 30 ( 1 ): 17 - 27 .

董立岩 , 隋鹏 , 孙鹏 , 等 . 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 ( 03 ).

DONG L Y , SUI P , SUN P , et al . Novel Naive Bayes classification al-gorithm based on semi-supervised learning [J ] . Journal of Jilin Univer-sity,Engineering and Technology Edition , 2016 ( 03 ).

罗福星 . 增量学习朴素贝叶斯中文分类系统的研究 [D ] . 长沙:中南大学 , 2008 .

LUO F X . Research on incremental learning naive Bayesian classifica-tion system [D ] . Changsha:Central South University , 2008 .

张智敏 . 基于增量学习的分类算法研究 [D ] . 广州:华南理工大学 , 2010 .

ZHANG Z M . The study of classification based on incremental learn-ing [D ] . Guangzhou:South China University of Technology , 2010 .

侯凯 . 加权贝叶斯增量学习中文文本分类研究 [D ] . 长沙:长沙理工大学 , 2013 .

HOU K . The weighted Bayesian incremental learning Chinese text classification study [D ] . Changsha:Changsha University of Science ＆Technology , 2013 .

李金华 , 梁永全 , 吕芳芳 . 一种加权朴素贝叶斯分类增量学习模型 [J ] . 计算机与现代化 , 2010 , 2010 ( 5 ): 30 - 32 .

LI J H , LIANG Y Q , LV F F . An incremental learning model of weighted Naive Bayesian cassification [J ] . Computer and Moderniza-tion , 2010 , 2010 ( 5 ): 30 - 32 .

罗福星 , 刘卫国 . 一种朴素贝叶斯分类增量学习算法 [J ] . 微计算机应用 , 2008 , 29 ( 6 ): 107 - 112 .

LUO F X , LIU W G . An incremental learning algorithm based on weighted Naive Bayes classification [J ] . Microcomputer Applications , 2008 , 29 ( 6 ): 107 - 112

宫秀军 , 刘少辉 , 史忠植 . 一种增量贝叶斯分类模型 [J ] . 计算机学报 , 2002 , 25 ( 6 ): 645 - 650 .

GONG X J , LIU S H , SHI Z Z . An incremental Bayes classification model [J ] . Chinese Journal of Computers , 2002 , 25 ( 6 ): 645 - 650

高洁 , 吉根林 . 一种增量式 Bayes 文本分类算法 [J ] . 南京师范大学学报(工程技术版) , 2004 , 4 ( 3 ): 49 - 52 .

GAO J , JI G L . Incremental Bayes text categorization algorithm [J ] . Journal of Nanjing Normal University(Engineering and Technology) , 2004 , 4 ( 3 ): 49 - 52 .

王小林 , 镇丽华 , 杨思春 , 等 . 基于增量式贝叶斯模型的中文问句分类研究 [J ] . 计算机工程 , 2014 , 40 ( 9 ): 238 - 242 .

WANG X L , ZHEN L H , YANG S C , et al . Chinese question classifi-cation research based on incremental Bayes model [J ] . Computer En-gineering , 2014 , 40 ( 9 ): 238 - 242 .

段华 . 支持向量机的增量学习算法研究 [D ] . 上海:上海交通大学 , 2008 .

DUAN H . Study on the incremental learning algorithms for support vector machines [D ] . Shanghai:Shanghai Jiao Tong University , 2008 .

姜卯生 , 王浩 , 姚宏亮 . 朴素贝叶斯增量学习序列算法研究 [J ] . 计算机工程与应用 , 2004 , 40 ( 14 ): 57 - 59 .

JIANG M S , WANG H , YAO H L . Studies on incremental learning sequence algorithm of Naive Bayesian classifier [J ] . Computer Engi-neering and Applications , 2004 , 40 ( 14 ): 57 - 59

ZHANG H , SHENG S . Learning weighted Naive Bayes with accurate ranking [C ] // Fourth IEEE International Conference on Data Mining . 2004 : 567 - 570 .

READ J , BIFET A , PFAHRINGER B , et al . Batch-in cremental versus instance-incremental learning in dynamic and evolving data [C ] // International Symposium on Intelligent Data Analysis . Springer Berlin Heidelberg , 2012 : 313 - 323

GU P , ZHU Q S , ZHANG C . A multi-view approach to semi-supervised document classification with incremental Naive Bayes [J ] . Computers＆ Mathematics with Applications , 2009 , 57 ( 6 ): 1030 - 1036 .

MUHLBAIER M D , TOPALIS A , POLIKARO R . NC:combining ensemble of classifiers with dynamically weighted consult-and-vote for efficient incremental learning of new classes [J ] . IEEE Transactions on Neural Networks , 2009 , 20 ( 1 ): 152 - 168 .

BOUGUELIA M , BELAÏD Y , BELAÏD A , . A stream-based semi-supervised active learning approach for document classification [C ] // 2013 12th International Conference on Document Analysis and Recognition . IEEE , 2013 : 611 - 615 .

FONG S,BIUK-AGHAI R P , SI Y , et al . A lightweight data preprocessing strategy with fast contradiction analysis for incremental classifier learning [J ] . Mathematical Problems in Engineering , 2015 , 2015 : 1 - 11 .

EYHERAMENDY S , LEWIS D D , MADIGAN D . On the Naive Bayes model for text categorization [J/OL ] . https://core.ac.uk/display/21543464 https://core.ac.uk/display/21543464 , 2003 .

PEDRO D , MICHAEL P . On the optimality of the simple Bayesian classifier under zero-one loss [J ] . Machine Learning , 1997 , 29 : 103 - 130 .

ANDREW M C , KAMAL N . A comparison of event models for Naive Bayes text classification [J ] . In AAAI-98 Workshop on Learning for Text Catego , 2009 , 62 ( 2 ): 41 - 48 .

AY N , JORDAN M . On discriminative vs generative classifiers:a comparison of logistic regression and Naive Bayes [J ] . Advances in Neural Information Processing Systems , 2002 , 2 ( 3 ): 169 - 187 .

ZHANG H , . The optimality of Naive Bayes [C ] // The Seventeenth International Florida Artificial Intelligence Research Society Conference . Miami Beach,Florida,USA , 2004 : 562 - 567 .

浏览量

1877

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

改进的朴素贝叶斯算法在垃圾邮件过滤中的研究

基于核方法的敏感信息过滤的研究