面向目标的带先验概率的AdaBoost算法

赵向辉; 姚宇; 付忠良; 苗青; 谢会云

您当前的位置：

首页 >

文章列表页 >

面向目标的带先验概率的AdaBoost算法

信息工程 | 更新时间：2023-11-14

- 面向目标的带先验概率的AdaBoost算法
- A Goal-oriented AdaBoost Algorithm with Prior Probabilities
- 工程科学与技术 2010年42卷第2期页码：139-144
- 作者机构：
  
  1. 中国科学院成都计算机应用研究所四川,成都,610041
  2. 中国科学院研究生院北京,100049
  3. 中国科学院成都计算机应用研究所
  4. 中国科学院研究生院
- 作者简介：
- 基金信息：
  
  国家重点基础研究发展规划（973计划）项目（2004CB318003）;四川省科技支撑计划资助项目（2008SZ0100;2009SZ0214);中国科学院西部之光人才培养项目资助
- DOI：
  中图分类号：
- 纸质出版日期：2010，
  
  网络出版日期：2009-11-25，
扫描看全文
赵向辉,姚宇,付忠良,苗青,谢会云.面向目标的带先验概率的AdaBoost算法[J].工程科学与技术,2010,42(2):139-144.

Zhao Xianghui, Yao Yu, Fu Zhongliang, et al. A Goal-oriented AdaBoost Algorithm with Prior Probabilities[J]. Advanced Engineering Sciences, 2010,42(2):139-144.
赵向辉,姚宇,付忠良,苗青,谢会云.面向目标的带先验概率的AdaBoost算法[J].工程科学与技术,2010,42(2):139-144. DOI：

Zhao Xianghui, Yao Yu, Fu Zhongliang, et al. A Goal-oriented AdaBoost Algorithm with Prior Probabilities[J]. Advanced Engineering Sciences, 2010,42(2):139-144. DOI：

摘要

中文摘要: 针对集成学习算法研究中多个分类器的最佳组合问题，本文改进了传统的AdaBoost集成学习算法。用于组合的各个分类器通常是基于样本集通过一定的训练得到，样本集中不同类目标的比率可以反映分类目标的先验概率。本文使用该参数给出了新的组合参数和投票表决阈值计算公式，巧妙的利用样本权值并将其加入到样本属性上进行训练学习，采用新的策略来选择基分类器，给出了面向目标的带先验概率的AdaBoost算法(GWPP AdaBoost算法)和分类器的最佳组合。依据UCI实验数据对传统的AdaBoost 算法、Bagging 算法、GWPP AdaBoost算法的错误率和性能进行了比较分析，验证了GWPP AdaBoost的有效性。

Abstract

Abstract:Aiming at the best combination of multiple classifiers in ensemble learning algorithm

this paper improves the traditional AdaBoost algorithm. The combined classifiers are obtained by training the sample set. Using the sample to centralize the ratio of different kinds of targets can reflect prior probability of various classifiers. Through utilizing this parameter

this paper has given new combination parameter and the computation formula of vote threshold. It uses the sample weight skillfully and adds it to sample attributes for training and learning. It adopts new strategies to select base classifiers and gives prior probabilities AdaBoost algorithm of goal-oriented (GWPP AdaBoost algorithm) and the best combination of multiple classifiers. We have made comparative analysis of the error rate and the performance of the ordinary AdaBoost algorithm，Bagging algorithm and the GWPP AdaBoost algorithm based on the UCI datasets. Experiments show the validity of the GWPP AdaBoost algorithms.

关键词

集成学习AdaBoost算法分类器组合先验概率

Keywords

ensemble learningAdaBoost algorithmcombination of classifiersprior probability

references

Vapnik, Vladimir N.,Overview of statistical learning theory,IEEE Transactions on Neural Networks ,1999, 10(5).

Vapnik V N,Statistical learning theory,New York:John Wiley and Sons,Inc,1998.

Schapire R E,A brief introduction of boosting,San Francisco:Morgan Kaufmann Publishers,1999.

Schapire R E;Freund Y;Bartlett Y,Boosting the margin:A new explanation for the effectiveness of voting methods,Annals of Statistics,1998(5).

Schapire R E;Rochery M;Rahim M,Boosting with prior knowledge for call classification,IEEE Transactions on Speech and Audio Processing,2004(2).

Xiaofan Lin ；Sherif Yacoub ；John Burns ；Steven Simske,Performance analysis of pattern classifier combination by plurality voting,Pattern Recognition Letters ,2003, 24(12).

燕继坤;郑辉;王艳.基于可信度的投票法[J].计算机学报,2005(8)

吕岳,施鹏飞,赵宇明.多分类器组合的投票表决规则[J].上海交通大学学报,2000(5)

付忠良.关于AdaBoost有效性的分析[J].计算机研究与发展,2008(10)

LEO BREIMAN,Random Forests,Machine Learning ,2001, 45(1).

Freund Y. ；Schapire RE.,A DECISION-THEORETIC GENERALIZATION OF ON-LINE LEARNING AND AN APPLICATION TO BOOSTING,Journal of Computer and System Sciences ,1997, 55(1).

Breiman L,Bagging predicators,Machine Learning,1996(2).

Ferreira C,Gene Expression Programming:A new adaptive algorithm for solving problems,Complex Systems,2001(2).

Holland J H,Adaptation in natural and artificial systems,Ann Arbor,MI:The University of Michigan Press,1975.

Wasserman P D,Neural computing:Theory and practice,New York:Van Nostrand Reinhold Company Inc,1989.

Blake C;Keogh E;Merz C,UCI repository of machine learning databases,Irvine,CA,University of California,1998.

Kearns M;Valiant L G,Crytographic limitation on learning Boolean formulae and finite automata,New York:ACM,1989.

Fu Zhongliang;Yao Yu;Zhao Xianghui,The best combining of classifiers with prior probabilities,Leipzig,Germany,2009.

Schapire R E,Theoretical views of boosting and applications,Tokyo Japan,1999.

Robert E;Schapire R E,The strength of weak learnability,Machine Learning,1990(2).

更多指标>

浏览量

下载量

CNKI被引量

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于混合梯度提升决策树和逻辑回归模型的分组密码算法识别方案

基于EBLOF算法的攻击者IP分析系统及应用

AdaBoost算法的推广——一组集成学习算法

基于集成学习方法的蛋白质相互作用预测