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一种基于差异思想的选择性Bagging Trees集成算法研究
  • ISSN号：1005-5762
  • 期刊名称：《统计教育》
  • 时间：0
  • 分类：TP314[自动化与计算机技术—计算机软件与理论;自动化与计算机技术—计算机科学与技术] TP311.13[自动化与计算机技术—计算机软件与理论;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, [2]西安财经学院, [3]中国建设银行北京首体南路支行
  • 相关基金：本文得到自然科学基金重点项目（#10431010）、教育部重点基地重大项目（#05JJD910001）及中国人民大学应用统计中心的资助.
作者： 陈凯[1], 朱钰[2], 王征[3]
关键词： 决策树, 自助法, 选择性集成, Decision Trees, Bootstrap, Selective Ensemble
中文摘要：

本文通过对鸢尾花数据的研究，提出了一种基于分类器的分类效果差异而进行快速选择的一种改进的Bagging Trees集成算法。并通过同其他统计机器学习方法，如：CART、Bagging Trees、Random Forest以及目前流行的基于遗传算法的选择性集成算法GASEN等比较得出，该算法对于分类问题而言，具有较高的准确率，而且与GASEN算法相比，运行的效率也得到了较大的提高。

英文摘要：

In this paper, based on a case study of iris dataset, it draws a new ensemble algorithm, a selective bagging trees ensemble based on diversity of different classifiers. And contrasted with other statistical machine learning methods, such as, CART, bagging trees, random forest and the current prevalent selective ensemble based on genetic algorithm, GASEN, this new algorithm proposed in this paper has higher accuracy, and also costs much little time than GASEN algorithm and improves efficiency when it is used in the problems of classification.

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期刊信息
  • 《统计教育》
  • 主管单位:国家统计局
  • 主办单位:国家统计局统计教育中心 中国统计教育学会
  • 主编：张仲梁
  • 地址：北京市西城区月坛南街57号
  • 邮编：100826
  • 邮箱：Tongjjy@163.com
  • 电话：010-63376220
  • 国际标准刊号：ISSN：1005-5762
  • 国内统一刊号：ISSN：11-3215/G4
  • 邮发代号:
  • 获奖情况:
  • 国内外数据库收录:
  • 被引量:4398