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基于分类问题的选择性集成学习研究
  • ISSN号：1001-3695
  • 期刊名称：《计算机应用研究》
  • 时间：0
  • 分类：TP301.6[自动化与计算机技术—计算机系统结构;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]中国人民大学统计学院,北京100872
  • 相关基金：国家自然科学基金重点资助项目（10431010）;国家教育部重点基地重大项目（05JJD910001）
作者： 陈凯[1]
关键词： 分类回归树, 自助法, 选择性集成, CART, bootstrap, selective ensemble
中文摘要：

提出了一种应用于分类问题，以分类回归树为基学习器，并综合了Ada Boost．M1和Bagging算法特点，利用变相似度聚类技术和贪婪算法来进行选择性集成学习的算法——SEC-Ada Boost Bagging Trees，并将其与几种常用的机器学习算法比较研究得出，该算法往往比其他算法具有更好的泛化性能和更高的运行效率。

英文摘要：

This paper introduced a new ensemble algorithm, SEC-AdaBoostBagging Trees ensemble algorithm, which was a combination of tree classifiers and was based on variational similarity cluster technology and greedy method, and it was also combined with the features of AdaBoost. M1 and Bagging. Compared with a series of other learning algorithms, it often has better generalization ability and higher efficiency.

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期刊信息
  • 《计算机应用研究》
  • 北大核心期刊（2011版）
  • 主管单位:四川省科学技术厅
  • 主办单位:四川省计算机研究院
  • 主编：刘营
  • 地址：成都市成科西路3号
  • 邮编：610041
  • 邮箱：arocmag@163.com
  • 电话：028-85210177 85249567
  • 国际标准刊号：ISSN：1001-3695
  • 国内统一刊号：ISSN：51-1196/TP
  • 邮发代号:62-68
  • 获奖情况:
  • 第二届国家期刊奖百种重点科技期刊,国内计算技术类重点核心期刊,国内外著名数据库收录期刊
  • 国内外数据库收录:
  • 俄罗斯文摘杂志,波兰哥白尼索引,英国科学文摘数据库,日本日本科学技术振兴机构数据库,中国中国科技核心期刊,中国北大核心期刊（2004版）,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）,中国北大核心期刊（2000版）
  • 被引量:60049