基于属性关联的朴素贝叶斯分类算法
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摘要
针对传统朴素贝叶斯分类算法处理多维连续型数据时准确率较低的问题,提出基于属性关联的改进算法。通过高斯分割对属性类别不同的多维连续型数据集进行离散化处理,并使用拉普拉斯校准、属性关联和属性加权方法改进朴素贝叶斯分类过程。实验结果表明,与基于拉普拉斯校准或属性加权的改进算法相比,该算法能够提高分类准确率,且提升幅度在一定范围内随着属性数量的增加而增加,适用于多维连续型数据的分类。
Aiming at the problem that the accuracy of the multi-dimensional continuous data is too low for traditional naive Bayesian classification algorithm,an improved classification algorithm based on attribute association is proposed.Directed against the multidimensional continuous data set with different attribute classes,it discretizes the data set by Gaussian segmentation,which is improved by using Laplace calibration,attribute association and weighted attribute.Experimental results show that,compared with improved algorithms by Laplace calibration or attribute weighting,the proposed algorithm can improve the accuracy of classification results,and its amplitude increase is increased with the increase of the number of attributes in a certain range,which is suitable for the classification of multidimensional continuous data.
Aiming at the problem that the accuracy of the multi-dimensional continuous data is too low for traditional naive Bayesian classification algorithm,an improved classification algorithm based on attribute association is proposed.Directed against the multidimensional continuous data set with different attribute classes,it discretizes the data set by Gaussian segmentation,which is improved by using Laplace calibration,attribute association and weighted attribute.Experimental results show that,compared with improved algorithms by Laplace calibration or attribute weighting,the proposed algorithm can improve the accuracy of classification results,and its amplitude increase is increased with the increase of the number of attributes in a certain range,which is suitable for the classification of multidimensional continuous data.
引文
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[15]俞杰,丁晓剑,崔鹏.关联规则挖掘以改进朴素贝叶斯[J].舰船电子工程,2016,36(5):112-117.
[16]毕佳佳,张晶.基于关系选择的多关系朴素贝叶斯分类[J].计算机工程,2016,42(5):218-223.
[2]JIANG Liangxiao,LI Chaoqun,WANG Shasha,et al.Deep feature weighting for naive Bayes and its application to text classification[J].Engineering Applications of Artificial Intelligence,2016,52(C):26-39.
[3]罗辛,欧阳元新,熊璋,等.通过相似度支持度优化基于K近邻的协同过滤算法[J].计算机学报,2010,33(8):1437-1445.
[4]CALDERS T,VERWER S.Three naive Bayes approaches for discrimination-free classification[J].Data Mining and Knowledge Discovery,2010,21(2):277-292.
[5]唐发明,王仲东,陈绵云.支持向量机多类分类算法研究[J].控制与决策,2005,20(7):746-749.
[6]PANDE S,MORGAN F,CAWLEY S,et al.Modular neural tile architecture for compact embedded hardware spiking neural network[J].Neural Processing Letters,2013,38(2):131-153.
[7]栾丽华,吉根林.决策树分类技术研究[J].计算机工程,2004,30(9):94-96.
[8]许立莎.基于关联规则挖掘的分类算法研究[D].西安:西安科技大学,2012.
[9]KARIMNEZHAD A,MORADI F.Bayes,E-Bayes and robust Bayes prediction of a future observation under precautionary prediction loss functions with applications[J].Applied Mathematical Modelling,2016,40(15/16):7051-7061.
[10]徐光美,刘宏哲,张敬尊,等.基于特征加权的多关系朴素贝叶斯分类模型[J].计算机科学,2014,41(10):283-285.
[11]江小平,李成华,向文,等.云计算环境下朴素贝叶斯文本分类算法的实现[J].计算机应用,2011,31(9):2551-2554,2566.
[12]顾晓清,王洪元,倪彤光,等.基于贝叶斯和支持向量机的钓鱼网站检测方法[J].计算机工程与应用,2015,51(4):87-90,95.
[13]汤贤娟.Apriori算法和贝叶斯分类器在多标记学习中的应用[D].马鞍山:安徽工业大学,2013.
[14]陈朝大,梁柱勋,郑士基.一种利用关联规则的改进朴素贝叶斯分类算法[J].计算机系统应用,2010,19(11):106-109.
[15]俞杰,丁晓剑,崔鹏.关联规则挖掘以改进朴素贝叶斯[J].舰船电子工程,2016,36(5):112-117.
[16]毕佳佳,张晶.基于关系选择的多关系朴素贝叶斯分类[J].计算机工程,2016,42(5):218-223.