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基于有序决策树的改进归纳算法
  • ISSN号：1672-3961
  • 期刊名称：《山东大学学报：工学版》
  • 时间：0
  • 分类：TP181[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程]
  • 作者机构：[1]河北大学数学与计算机学院,河北保定071002, [2]河北省机器学习与计算智能重点实验室,河北保定071002
  • 相关基金：国家自然科学基金资助项目（61170040）;河北省自然科学基金资助项目（F2013201110,F2013201220）;河北大学自然科学基金资助项目（2011-228043）
作者： 潘盼[1], 王熙照[2], 翟俊海[2]
关键词： 有序互信息, 有序分类, 有序信息熵, 属性相关, 决策树, ranking mutual information, ordinal classification, ranking entropy, correlation of attribute, decision tree
中文摘要：

基于构建有序决策树，提出了一种新的归纳算法。该算法选择的扩展属性不仅和类的有序互信息值最大，而且要求和同一分支上已被用过的条件属性的有序互信息值最小。实验结果表明，考虑了条件属性之间的相关性后，可避免同一条件属性的重复选择，真正体现了条件属性和决策属性之间的有序互信息，与已有的算法相比，提高了测试精度。

英文摘要：

An improved ordinal decision tree algorithm was proposed.The extended attributes selected with the pro-posed algorithm maximized the ranking mutual information between the candidate attributes and the decision attribute, and also minimized the ranking mutual information between the candidate attributes and the selected conditional attrib-utes on the same branch.The experimental results showed that the correlation to be taken account among the conditional attributes could avoid to selecte the same one, and the ideas of the proposed method could really reflect the nature of the ranking mutual information.The proposed algorithm could improve the test accuracy compared with the existing algo-rithms.

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期刊信息
  • 《山东大学学报：工学版》
  • 北大核心期刊（2011版）
  • 主管单位:教育部
  • 主办单位:山东大学
  • 主编：李术才
  • 地址：山东济南市经十路17923号
  • 邮编：250061
  • 邮箱：xbgxb@sdu.edu.cn
  • 电话：0531-88396452
  • 国际标准刊号：ISSN：1672-3961
  • 国内统一刊号：ISSN：37-1391/T
  • 邮发代号:24-221
  • 获奖情况:
  • 国内外数据库收录:
  • 美国化学文摘（网络版）,波兰哥白尼索引,美国剑桥科学文摘,中国中国科技核心期刊,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）
  • 被引量:6258