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主动贝叶斯分类方法研究 被引量：1

Study of an Active Bayesian Classifier Method
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摘要 在对实际数据进行分类求解时,往往会遇到大量未带类别标注的样本,现有的经典分类方法常采用先标注缺失样本,再进行分类,存在耗时且分类精度差等问题.为此,提出一种基于主动学习思想贝叶斯分类方法RANB. 引入主动学习旨在减少评价样本所需代价,提高分类器性能. RANB方法在主动学习策略的基础上融入条件熵和分类损失的思想,可以有效抑制不确定样本所带来的误差.实验表明,该方法与朴素贝叶斯分类器等经典方法相比,在保证分类性能的前提下,可有效地减少学习所需的样本数量,尤其是对于未带类别标志的样本,更是有其优越性.
作者 李笛 胡学钢 胡春玲
机构地区 合肥工业大学计算机与信息学院
出处 《计算机研究与发展》 EI CSCD 北大核心 2007年第z2期47-51,共5页 Journal of Computer Research and Development
基金 安徽省自然科学基金项目(050420207)
关键词 数据挖掘 机器学习 朴素贝叶斯分类器 主动学习 条件熵
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献9

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共引文献41

同被引文献13

  • 1Yang Jianwu, Cheung W K, Chen Xiaoou. Integrating element and term semantics for similarity-based XML document clustering[ C ]//Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence. 2005: 222-228.
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  • 3Mezghani N, Mitiche A, Cheriet M. Bayes classification of online Arabic characters by Gibbs modeling of class conditional densities[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence ,2008,30 ( 7 ) : 1121-1131.
  • 4Xiangrong Shi ,Jun Liang,Lubin Ye,et al. A method of fault diagnosis based on PCA and Bayes classification[ C]//Proceedings of the 2010 8th World Congress on Intelligent Control and Automation(WCICA). 2010:5628-5631.
  • 5Iqbal K, Asghar S, Fong S. Hiding sensitive XML association rules via Bayesian network [ C ]//Proceedings of the 2010 6th International Conference on Advanced Information Management and Service(IMS). 2010:466-471.
  • 6Chang C C, Lu Hsueh-Ming. Integration of heterogeneous medical decision support systems based on Web services [ C ]//Proceedings of the 2009 Ninth IEEE International Conference on Bioinformatics and Bioengineering. 2009: 415-422.
  • 7阎红灿,王淑芬,朱晓亮,李敏强,刘保相.基于TreeMiner算法的XML文档结构相似度量方法[J].计算机应用研究,2009,26(5):1706-1709. 被引量：2
  • 8刘喜平,万常选.有效的XML检索结果的相似性度量[J].南京大学学报（自然科学版）,2009,45(5):629-637. 被引量：3
  • 9余宏,万常选.基于XML的检索结果聚类方法[J].计算机工程,2010,36(1):85-86. 被引量：5
  • 10朴勇,田伟,王秀坤.一种XML文档结构相似度计算方法[J].控制与决策,2010,25(4):497-501. 被引量：4

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计算机研究与发展
2007年 第z2期

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