《计算机应用研究》|Application Research of Computers

基于引力模型的朴素贝叶斯分类算法

Naive Bayesian classification algorithm based on gravity model

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作者 王威,赵思逸,王新
机构 长沙理工大学 计算机与通信工程学院,长沙 410114
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文章编号 1001-3695(2018)09-2602-03
DOI 10.3969/j.issn.1001-3695.2018.09.009
摘要 针对朴素贝叶斯分类器在分类过程中不同类别的同一特征量之间由于存在相似性,易导致误分类的现象,提出基于引力模型的朴素贝叶斯分类算法。提出以引力公式中距离变量的平方作为相似距离,应用引力模型来刻画特征与其所属类别之间的相似度,从而克服朴素贝叶斯分类算法容易受到条件独立假设的影响而将所有特征同质化的缺点,并能有效地避免噪声干扰,达到修正先验概率、提高分类精度的目的。对遥感图像的分类实验表明,基于引力模型的朴素贝叶斯分类算法易于实现、可操作性强,且具有更高的平均分类准确率。
关键词 分类算法;朴素贝叶斯;引力模型;遥感图像
基金项目 国家重大基础研究项目(613XXX0301)
本文URL http://www.arocmag.com/article/01-2018-09-009.html
英文标题 Naive Bayesian classification algorithm based on gravity model
作者英文名 Wang Wei, Zhao Siyi, Wang Xin
机构英文名 SchoolofComputer&CommunicationEngineering,ChangshaUniversityofScience&Technology,Changsha410114,China
英文摘要 In order to solve the problem of misclassified in the process of naive Bayesian classifier which caused by the similarity between the same feature quantities of different categories, this paper presented a simple Bayesian classification algorithm based on gravitational model.This algorithm could overcome the influence of the naive Bayesian classification algorithm, which easy to be influenced by effectively avoid noise interference, correct the prior probabilities, and could improved the accuracy of classification purposes.This paper proposed a gravitational model to describe the similarity between the feature and its category by using the square of the distance variable in the gravitational formula as the similar distance.The classification experiments of remote sensing images show that the naive Bayesian classification algorithm based on gravitational model is easy to implement, has high operability and has higher average classification accuracy.
英文关键词 classification algorithm; naive Bayesian; gravitational model; remote sensing image
参考文献 查看稿件参考文献
  [1] Domingos P, Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss[J] . Machine Learning, 1997, 29(2):103-130.
[2] McCallum A, Nigam K. A comparison of event models for naive Bayes text classification[C] //Proc of AAAI Workshop on Learning for Text Categorization. 1998:41-48.
[3] Tarabalka Y, Fauvel M, Chanussot J, et al. SVM-and MRF-based method for accurate classification of hyperspectral images[J] . IEEE Geoscience & Remote Sensing Letters, 2010, 7(4):736-740.
[4] LeDoux J E. Emotion circuits in the brain[J] . Annual Review of Neuroscience, 2000, 23(2):155-184.
[5] Kulkarni A R, Tokekar V, Kulkarni P. Identifying context of text documents using nave Bayes classification and Apriori association rule mining[C] //Proc of the 6th International Conference on Software Engineering. Piscataway, NJ:IEEE Press, 2012:1-4.
[6] 程环环. 基于贝叶斯网络的图像内容表述与分类[D] . 长沙:国防科学技术大学, 2011.
[7] 李静梅, 孙丽华, 张巧荣, 等. 一种文本处理中的朴素贝叶斯分类器[J] . 哈尔滨工程大学学报, 2003, 24(1):71-74.
[8] 李方, 刘琼荪. 基于改进属性加权的朴素贝叶斯分类模型[J] . 计算机工程与应用, 2010, 46(4):132-133.
[9] 周喜. 基于粗糙集的加权朴素贝叶斯分类算法研究[D] . 长沙:长沙理工大学, 2013.
[10] 王行甫, 付欢欢, 王琳. 基于余弦相似度和实例加权改进的贝叶斯算法[J] . 计算机系统应用, 2016, 25(8):166-170.
[11] Shati S P, Hossain M D, Nadim M, et al. Enhancing performance of nave Bayes in text classification by introducing an extra weight using less number of training examples[C] //Proc of International Workshop on Computational Intelligence. Piscataway, NJ:IEEE Press, 2016:142-147.
[12] 赵柳. 相对论与引力理论导论[M] . 北京:科学出版社, 2016.
[13] 李硕豪, 张军. 贝叶斯网络结构学习综述[J] . 计算机应用研究, 2015, 32(3):641-646.
[14] 慕春棣, 戴剑彬, 叶俊. 用于数据挖掘的贝叶斯网络[J] . 软件学报, 2000, 11(5):660-666.
收稿日期 2017/4/25
修回日期 2017/6/2
页码 2602-2604
中图分类号 TP391;TP301.6
文献标志码 A