基于ICA的多维贝叶斯分类器

摘要/Abstract

摘要：

多维贝叶斯分类器是处理多维分类问题的概率图形模型,其中属性变量可决定一个或多个类变量。文中针对属性变量维数较高和信息冗余问题,采用Fast ICA算法对属性变量进行降维,从而将高维属性变量约减为能较完整描述数据信息的低维属性变量。然后根据约减后的属性变量构建多维贝叶斯分类器;最终,通过理论分析得到基于ICA的多维贝叶斯分类器的性能较好。实验结果表明,对3组基准数据集的分类,基于ICA的多维贝叶斯分类器相比于其他算法具有较高的分类准确率。

关键词: 贝叶斯网络, 多维贝叶斯分类器, 独立成分分析, 互信息

Abstract:

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models proposed to deal with multi-dimensional classification problems,where each feature variable determines one or more than one class variable.For the problem of high dimensional of feature attributes and information redundancy,the Independent Component Analysis (ICA) is applied to decrease the dimension of feature variable which could completely describe data.Then,we construct a multi-dimensional Bayesian network classifier according to the decreased data.Finally,the performance of the MBCs is proved good by theoretical analysis.The experiment results show that for three benchmark multi-dimensional data sets,the multi-dimensional Bayesian network classifier based on ICA outperforms other algorithms in accuracy.

Key words: bayesian network;multi-dimensional bayesian network classifiers;independent component analysis;mutual information

中图分类号:

唐兴佳, 张秀方. 基于ICA的多维贝叶斯分类器[J]. , 2014, 27(2): 25-.

TANG Xin-Jia, ZHANG Xiu-Fang. Multi-dimensional Bayesian Network Classifiers Based on ICA[J]. , 2014, 27(2): 25-.

参考文献

［1］TSOUMAKAS G,KATAKIS I,VLAHAVAS I.Mining multi-label data［M］.Springer:Data Mining and Knowledge Discovery Handbook,2010.

［2］BIELZA C,LI G,LARRANAGA P.Multi-dimensional classification with bayesian networks［J］.International Journal of Approximate Reasoning,2011(52):705-727.

［3］YE Jimin,HUANG Ting.New Fast-ICA algorithms for blind source separation without prewhitening［J］.Communications in Computer and Information Science,2011,255(2):579-585.

［4］PEARL J.Probabilistic reasoning in intelligent systems:networks of plausible inference［M］.Morgan:Morgan Kaufmann Publishers,1988.

［5］KOLLER D,FRIEDMAN N.Probabilistic graphical models principles and techniques［M］.Cambridge:MIT Press,2009.

［6］VAN DER GAAG L,DE WAAL P R.Multi-dimensional bayesian network classifiers［C］.Shoul:Third European Conference on Probabilistic Graphical Models,2006:107-114.

［7］DE WAAL P R,VAN DER GAAG L C.Inference and learning in multi-dimensional bayesian network classifiers［C］.European Conference on Symbolic and Quantitative Approaches to Reasoning under Uncertainty,Lecture Notes in Artificial Intelligence,2007,4724:501-511.

［8］RODRGUEZ J D,LOZANO J A.Multi-objective learning of multi-dimensional Bayesian classifiers［C］.Beijing:Proceedings of the Eighth International Conference on Hybrid Intelligent Systems,2008:501-506.

［9］PROAKIS J.Digital communication［M］.Columbus,USA:MeGraw-Hill,2000.

［10］TSOCHANTARIDIS I,JOACHIMS T,HOFMANN T,et al.Large margin methods for structure d and interdependent output variables［J］.Journal o f Machine Learning Research,2005(6):1453-1484.

［11］BOUTELL M R,LUO J,SHEN X,et al.Learning multi-label scene classification［J］.Pattern Recognition,2004,37(9):1757-1771.

［12］PAVLIDIS P,WESTON J,CAI J,et al.A kernel method for multi-labelled classification［J］.Advances in Neural Information Processing Systems,MIT Press,2002(14):681-687.

［13］COOPER G F,HERSKOVITS E A.Bayesian method for the induction of probabilistic networks from data［J］.Machine Learning,1992(9):309-347.

［14］ZHANG Minling,ZHOU Zhihua.Multi-label neural networks with applications to functional genomics and text categorization［J］.IEEE Transactions on Knowledge and Data Engineering,2006,18(10):1338-1351.

［15］HUANG J,CAI Y,XU X.A filter approach to feature selection based on mutual information［C］.Beijing:Proceedings of the Fifth IEEE International Conference on Cognitive Informatics,2006:84-89.

［16］LIU Yue,YIN Yafeng,GAO Junjun,et al.Wrapper feature selection optimized SVM model for demand forecasting［C］.Hunan:The International Conference onYoung Computer Scientists,2008:953-958.

［17］HUI-HUANG HSU,CHENG-WEI HSIEH,MING-DA LU.Hybrid feature selection by combining filters and wrappers［J］.Expert Systems with Applications,2011,38(3):8144-8150.

[1]	仝兆景,秦紫霓,赵运星,芦彤,郑权. 基于贝叶斯网络的变压器故障诊断研究[J]. 电子科技, 2021, 34(3): 43-47.
[2]	郝晓平. 基于贝叶斯网络的研究生入学奖学金评定[J]. , 2016, 29(9): 34-.
[3]	张秀方,唐兴佳. 贝叶斯网络应用中的结构学习方法研究[J]. , 2014, 27(4): 179-.
[4]	张燕,朱明敏,宋苏鸣. 基于混合方式的贝叶斯网络结构学习[J]. , 2014, 27(10): 115-.
[5]	张庆锐. 一种新的自适应步长ICA算法[J]. , 2013, 26(1): 123-.
[6]	匡雅斌, 王敬东, 李鹏. 红外与可见光图像配准算法[J]. , 2011, 24(5): 80-.
[7]	李琼, 王军宁. 基于互信息和自适应模板更新的目标跟踪算法[J]. , 2011, 24(12): 25-.