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Any-Cost Discovery:优化分类规则发现方法

Any-Cost Discovery: Learning Optimal Classification Rules
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摘要 充分利用缺失数据中所包含的信息,提出了一种新的分裂属性的选择标准建立决策树.用户提供一定的资源后,通过访问该决策树,最大限度地利用该资源,以达到在有限资源约束下的最优结果.另外,该方法还可以对于需要追加资源与否提出建议. Fully taking into account the hints possibly hidden in the missing data, this paper proposes a new criterion when selecting attributes for splitting to build a decision tree for a given dataset. When con- sumer offers finite resources, we can make the best of the finite resources and obtain optimal results by the tree. In addition, we also put forward advice about whether it is worthy of increasing resources or not.
作者 钟智
机构地区 广西师范学院数学与计算机科学系
出处 《广西师范学院学报（自然科学版）》 2005年第3期76-79,84,共5页 Journal of Guangxi Teachers Education University(Natural Science Edition)
关键词 任意资源 决策树 不完备数据 代价敏感度 any cost decision tree missing data cost-sensitive
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献6

  • 1ZHANG Shi-chao, QIN Zhen-xing, ZHANG Cheng-qi. Cost-Sensitive Decision Trees with Multiple Cost Scales[J]. A12004: 380-390.
  • 2CHAI Xiao-yong, DENG Lin, YANG Qiang , Charles X Ling. Test-Cost Sensitive Naive Bayes Classification[J]. Proceedings of The IEEE International Conference on Data Mining, (ICDM'2004).
  • 3ZHANG Shi-chao, QIN Zhen-xing, Charles X Ling ,SHENG Sheng-li. Missing is Useful: Missing Values in Cost-sensitive Decision Trees[M]. IEEE Transactions on Knowledge and Data Engineering, 2005.
  • 4Turney P D. Cost-Seensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm [J]. Journal of Artificial Intelligence Research, 1995.
  • 5ZHANG Shi-chao YANG Qiang, WANG Jian-ning, Charles X Ling. Decision Trees with Minimal Costs[M]. Proceedings of the 21th International Conference on Machine Learning(ICML), Banff, Canada, 2004.
  • 6元昌安,唐常杰,左劼,谢方军,陈安龙,胡建军.基于基因表达式编程的函数挖掘——收敛性分析与残差制导进化算法[J].四川大学学报（工程科学版）,2004,36(6):100-105. 被引量：45

二级参考文献10

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共引文献44

广西师范学院学报（自然科学版）
2005年 第3期

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