Computer Science ›› 2014, Vol. 41 ›› Issue (10): 283-285.doi: 10.11896/j.issn.1002-137X.2014.10.059

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Multi-relational Nave Bayesian Classifier Using Feature Weighting

XU Guang-mei,LIU Hong-zhe and ZHANG Jing-zun   

  • Online:2018-11-14 Published:2018-11-14

Abstract: To improve the accuracy of multi-relational nave Bayesian classifiers,this paper discussed existing feature weighting methods and upgraded the method to deal with multi-relational data directly.Based on the tuple ID propagation method and counting methods towards tuples,a multi-relational nave Bayesian classifier using feature weighting (MRNBC-W) was given.Experiments on Financial database show that with the help of feature weighting,the classifiers can give better accuracy without increase of time complexity.Furthermore,MRNBC-W based on mutual information(MRNBC-W-MI) was implemented.

Key words: Multi-relational data mining(MRDM),Nave Bayes,Classification,Mutual information,Feature weighting

[1] Ratanamahatana C A,Gunopulos D.Scaling up the Naive Bayesian Classifier:Using Decision Trees for Feature Selection[C]∥Proceedings of Workshop on Data Cleaning and Preprocessing,at IEEE International Conference on Data Mining.Maebashi,Japan,2002
[2] 石洪波,王志海,黄厚宽,等.一种限定性的双层贝叶斯分类模型[J].软件学报,2004,5(2):193-199
[3] Ong H C,Khoo M Y,Saw S L.An Improvement on the Nave Bayes Classifier[C]∥International Conference on Information and Knowledge Management (2012).Singapore,2012:190-194
[4] Yin Xiao-xin,Han Jia-wei,Yang Jiong,et al.Efficient classification across multiple database relations:a CrossMine approach[J].IEEE Transactions on Knowledge and Data Engineering,2006,18(6):770-783
[5] Liu Hong-yan,Yin Xiao-xin,Han Jia-wei.An efficient multi-relational naive bayesian classifier based on semantic relationship graphs[C]∥Proceedings of the 4th international workshop on Multi-relational mining.Chicago,Illinois,2005:39-48
[6] 徐光美,杨炳儒,秦奕青,等.基于互信息的多关系朴素贝叶斯分类器[J].北京科技大学学报,2008,0(8):963-966
[7] 邓维斌,王国胤,王燕.基于Rough Set 的加权朴素贝叶斯分类算法[J].计算机科学,2007,34(2):204-206
[8] 邓维斌,王国胤,洪智勇.基于粗糙集的加权朴素贝叶斯邮件过滤方法[J].计算机科学,2011,8(2):218-221
[9] 王国才,张聪.一种基于粗糙集的特征加权朴素贝叶斯分类器[J].重庆理工大学学报,2010,4(7):86-90
[10] Zhang H,Sheng Li.Learning Weighted Naive Bayes with accurate ranking[C]∥The 4th IEEE International Conference on Data Mining (ICDM04).Brighton:IEEE Computer Society,2004:567-570
[11] 邓春伟,史焕卿.Lucene的最小风险概率加权朴素贝叶斯算法[J].哈尔滨理工大学学报,2012,7(1):63-67
[12] 张明卫,王波,张斌,等.基于相关系数的加权朴素贝叶斯分类算法[J].东北大学学报,2008,29(7):952-955
[13] Guo Bao-en,Liu Hai-tao.Assigning Hybrid-Weight for Feature Attribute in Nave Bayesian Classifier[C]∥2012 International Proceedings of Computer Science and Information Technology(2012).Hongkong,2012:86-90
[14] 张步良.基于分类概率加权的朴素贝叶斯分类方法[J].重庆理工大学学报,2012,26(7):81-83
[15] 杨敏,贺兴时,刘平丽,等.基于属性约简的PLS加权朴素贝叶斯分类[J].西安工程大学学报,2013,7(1):118-121
[16] 程克非,张聪.基于特征加权的朴素贝叶斯分类器[J].计算机仿真,2006,23(10):92-96
[17] 华锐,梁娜.特征加权朴素贝叶斯分类器在小样本中的应用[J].统计与决策,2012,23:69-71
[18] Frank E,Hall M,Pfahringer B.Locally weighted naive Bayes[C]∥The 19th Conference in Uncertainty in Artificial Intelligence(2003).Acapulco,Mexico:Morgan Kaufmann,2003:249-256
[19] Hall M.A decision tree-based attribute weighting filter for Naive Bayes[C]∥Knowledge-Based Systems.2007:120-126
[20] 张步良.基于分类概率加权的朴素贝叶斯分类方法[J].重庆理工大学学报:自然科学版,2012,26(7):81-83

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