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一种基于ACO的代价敏感集成分类器 被引量：2

A novel cost-sensitive ensemble classifier
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摘要 提出了一种自适应代价优化算法ACO,利用'登山式'方法查找最适合重采样数据子集的最优误分类代价值用于建立基分类器,克服了固定式误分类代价不尽科学和客观的缺点,利用重采样技术实现了数据集样本不足时的分类器训练.通过'投票'方式对原始数据集中的实例重新标记类标,学习得到一个适应于类分布不均衡数据集的自适应的集成分类器.实验证明,用自适应代价优化算法实现的分类器在类分布不均衡的数据集上的分类性能明显优于CSC,MetaCost和naive Bayes等建立的分类器. A novel adaptive cost-sensitive classification algorithm, named ACO（adaptive cost optimization） ,is proposed,in which the ＂hill-climbing＂ method is used to search the optimal misclassification cost in a feasible misclassification cost space to build base classifiers on each sub-dataset. The re-sam- pling technique was adopted to train cost-insensitive classifiers when confronted with insufficient learning examples. By relabeling the labels of the original data sets, the ACO algorithm learns adaptive cost-sensitive composite the self-adapted classifiers to class imbalanced data sets. We empirically evaluateed ACO with CSC, MetaCost and na＇/＇ve Bayes on some common class imbalanced data sets, and find the performance of ACO significantly outperform the others.
作者 陈晓林 宋恩民 马光志
机构地区 华中科技大学计算机科学与技术学院 郧阳医学院公共管理学院
出处 《华中科技大学学报（自然科学版）》 EI CAS CSCD 北大核心 2010年第10期5-8,共4页 Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金 中国国际科技合作基金资助项目(2009DFA12290)
关键词 机器学习 代价敏感 误分类代价 优化 集成分类器 machine learning cost-sensitive misclassification cost optimization ensemble classifier
分类号 TP391.6 [自动化与计算机技术—计算机应用技术]
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参考文献10

  • 1Turney P. Types of cost in inductive concept learning [C]// Workshop on Cost-Sensitive Learning at the Seventeenth International Conference on Machine Learning. Palo Alto: Stanford University, 2000: 1- 7.
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同被引文献20

  • 1WANG Haixun, FAN Wei, YU P S, et al. Mining con- cept-drifting data streams using ensemble classifiers [ C ]// Proceedings of Ninth International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2003:226-235.
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  • 3BAUER E, KOHAVI R. An empirical comparison of voting classification algorithms : bagging, boosting, and vari- ants[J]. Machine Learning, 1999, 36 (1-2) :105-139.
  • 4AHMED A, MOHAMED D. A new technique for com- bining multiple classifiers using the dempster-shafer theory of evidence [ J ]. Journal of Artificial Intelligence Research, 2002, 17:333-361.
  • 5ZHAO Qiangli, JIANG Yanghuang, XU Ming. Incremental learning by heterogeneous bagging ensemble [ C ]// Proceedings of the Sixth International Conference on Advanced Data Mining and Applications. Berlin: Springer- Verlag, 2010 : 1-12.
  • 6DAVY S, EDWIN L, HENDRIK V B. Towards incre- mental classifier fusion [ J ]. Intelligent Data Analysis, 2010, 14(1) :3-30.
  • 7MICHAEL D M, APOSTOLOS T, ROBI P. Combining ensemble of classifiers with dynamically weighted consult- and-vote for efficient incremental learning of new classes [J]. IEEE Transactions on Neural Networks, 2009, 20(1):152-168.
  • 8LIOR R. Ensemble-based classifiers[J].Artificial Intelligence Review, 2010, 33(1-2):1-39.
  • 9LIU Huan, ORBAN D. Cloud computing for large-scale data-intensive batch applications [ C ]//Proceedings of the Eighth IEEE International Symposium on Cluster Compu- ting and the Grid. Lyon: IEEE Press, 2008: 295-305.
  • 10DEAN J, GHEMAWAT S. MapReduce: simplified data processing on large clusters [ J ]. Communications of the ACM, 2008, 51(1) : 107-113.

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华中科技大学学报（自然科学版）
2010年 第10期

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