Classification method for interval uncertain data based on improved naive Bayes

doi:10.11772/j.issn.1001-9081.2014.11.3268

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (11): 3268-3272.DOI: 10.11772/j.issn.1001-9081.2014.11.3268

• Artificial intelligence • Previous Articles Next Articles

Classification method for interval uncertain data based on improved naive Bayes

LI Wenjin¹,XIONG Xiaofeng¹,MAO Yimin²

1. School of Science, Jiangxi University of Science and Technology, Ganzhou Jiangxi 341000, China;
2. College of Applied Science, Jiangxi University of Science and Technology, Ganzhou Jiangxi 341000,China

Received:2014-06-05 Revised:2014-06-30 Online:2014-12-01 Published:2014-11-01
Contact: LI Wenjin

基于改进朴素贝叶斯的区间不确定性数据分类方法

李文进¹,熊小峰¹,毛伊敏²

1. 江西理工大学理学院,江西赣州 341000
2. 江西理工大学应用科学学院,江西赣州 341000

通讯作者: 李文进
作者简介:李文进(1988-),男,江苏赣榆人,硕士研究生,主要研究方向:数据挖掘;熊小峰(1965-),男,江西赣州人,教授,主要研究方向:建模及应用软件、应用数理统计;毛伊敏(1970-),女,新疆伊宁人,副教授,博士,主要研究方向:数据挖掘。
基金资助:
国家自然科学基金资助项目;江西省自然科学基金资助项目

Abstract

Abstract:

Considering the high computation complexity and storage requirement of Naive Bayes (NB) based on Parzen Window Estimation (PWE), especially for classification on interval uncertain data, an improved method named IU-PNBC was proposed for classifying the interval uncertain data. Firstly, Class-Conditional Probability Density Function (CCPDF) was estimated by using PWE. Secondly, an approximate function for CCPDF was obtained by using algebraic interpolation. Finally, the posterior probability was computed and used for classification by using the approximate interpolation function. Artificial simulation data and UCI standard dataset were used to assume the rationality of the proposed algorithm and the affection of the interpolation points to classification accuracy of IU-PNBC. The experimental results show that: when the interpolation points are more than 15, the accuracy of IU-PNBC tends to be stable, and the accuracy increases with the increase of the interpolation points; IU-PNBC can avoid the dependence on the training samples and improve the computation efficiency effectively. Thus, IU-PNBC is suitable for classification on large interval uncertain data with lower computation complexity and storage requirement than NB based on Parzen window estimation.

摘要：

基于Parzen窗的朴素贝叶斯在区间不确定性数据分类中存在计算复杂度高、空间需求大的不足。针对该问题,提出一种改进的区间不确定性数据分类方法IU-PNBC。首先采用Parzen窗估计区间样本的类条件概率密度函数(CCPDF)；然后通过代数插值得到类条件概率密度函数的近似函数；最后利用近似代数插值函数计算样本的后验概率, 并用于预测。通过人工生成的仿真数据和UCI标准数据集验证了算法假设的合理性以及插值点数对IU-PNBC算法分类精度的影响。实验结果表明,当插值点数大于15时,IU-PNBC算法的分类精度趋于稳定,且插值点数越多,算法分类精度越高；该算法可以避免原Parzen窗估计对训练样本的依赖, 并有效降低计算复杂度；同时由于该算法具有远低于基于Parzen窗的朴素贝叶斯的运行时间和空间需求, 因此适合解决数据量较大的区间不确定性数据分类问题。

CLC Number:

TP18

LI Wenjin XIONG Xiaofeng MAO Yimin. Classification method for interval uncertain data based on improved naive Bayes[J]. Journal of Computer Applications, 2014, 34(11): 3268-3272.

李文进熊小峰毛伊敏. 基于改进朴素贝叶斯的区间不确定性数据分类方法[J]. 计算机应用, 2014, 34(11): 3268-3272.

References

[1]AGGARWAL C C, YU P S. A survey of uncertain data algorithms and applications [J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(5): 609-623.
[2]ZHOU A, JIN C, WANG G, et al. A survey on the management of uncertain data [J]. Chinese Journal of Computers, 2009, 32(1):1-16.(周傲英,金澈清,王国仁,等.不确定性数据管理技术研究综述[J].计算机学报,2009,32(1):1-16.)
[3]REN S. Interval number-based uncertain data mining and its applications [D]. Hangzhou: Zhejiang University, 2006.(任世锦.基于区间数的不确定性数据挖掘及其应用研究[D].杭州: 浙江大学,2006.)
[4]YANG J Q, GUNN S. Exploiting uncertain data in support vector classification[C]// 〖HJ1.8mm〗KES 2007: Proceedings of the 11th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, LNCS 4694. Berlin: Springer-Verlag, 2007: 148-155.
[5]QIN B, XIA Y, PRABHAKAR S, et al. A rule-based classification algorithm for uncertain data[C]// ICDE 2009: Proceedings of the 25th IEEE International Conference on Data Engineering. Piscataway: IEEE Press, 2009,23(1): 1633-1640.
[6]TSANG S, KAO B, YIP K Y, et al. Decision trees for uncertain data[J]. IEEE Transactions on Knowledge and Data Engineering, 2011, 23(1): 64-78.
[7]LI F, LI Y, WANG C. Uncertain data decision tree classification algorithm[J]. Journal of Computer Applications, 2009,29(11):3092-3095.(李芳,李一媛,王冲.不确定数据的决策树分类算法[J].计算机应用,2009,29(11):3092-3095.)
[8]REN J, LEE S D, CHEN X L, et al. Naive Bayes classification of uncertain data [C]// ICDM 2009: Proceedings of the Ninth IEEE International Conference on Data Mining. Piscataway: IEEE Press, 2009: 944-949.
[9]QIN B, XIA Y, WANG S, et al. A novel Bayesian classification for uncertain data [J]. Knowledge-Based Systems, 2011, 24(8): 1151-1158.
[10]JIANG L. Research on naive Bayes classifiers and its improved algorithms[D].Wuhan: China University of Geoscience,2009:4-66.(蒋良孝,朴素贝叶斯分类器及其改进算法研究[D].武汉:中国地质大学,2009:4-66.)
[11]WANG S, DU R, LIU Y. The learning and optimization of full Bayes classifiers with continuous attributes [J]. Chinese Journal of Computers, 2012, 35(10): 2129-2138.(王双成,杜瑞杰,刘颖.连续属性完全贝叶斯分类器的学习与优化[J].计算机学报, 2012,35(10):2129-2138.)
[12]YAN W, REN Z, ZHAO X, et al. Probabilistic photovoltaic power modeling based on nonparametric kernel density estimation[J]. Automation of Electric Power Systems,2013,37(10):1-6.(颜伟,任洲洋,赵霞,等.光伏电源输出功率的非参数核密度估计模型[J].电力系统自动化,2013,37(10):1-6.)
[13]LIU B, YANG Y, WEBB G I, et al. A comparative study of bandwidth choice in kernel density estimation for naive Bayesian classification[C]// PAKDD 2009: Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, LNCS 5476. Berlin: Springer-Verlag, 2009: 302-313.
[14]SILVERMAN B W. Density estimation for statistics and data analysis[M]. London: Chapman and Hall, 1986.
[15]OLSON L N, SCHRODER J B, TUMINARO R S. A general interpolation strategy for algebraic multigrid using energy minimization[J]. SIAM Journal on Scientific Computing, 2011, 33(2): 966-991.
[16]ZHAO Y, LIN H, BAO H. Local progressive interpolation for subdivision surface fitting[J]. Journal of Computer Research and Development, 2012, 49(8): 1699-1707.(赵宇, 蔺宏伟, 鲍虎军. 细分曲面拟合的局部渐进插值方法[J]. 计算机研究与发展, 2012, 49(8): 1699-1707.)
[17]MAELAND E. On the comparison of interpolation methods[J]. IEEE Transactions on Medical Imaging, 1988, 7(3): 213-217.

Classification method for interval uncertain data based on improved naive Bayes

基于改进朴素贝叶斯的区间不确定性数据分类方法

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