期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于改进网格搜索算法的随机森林参数优化 被引量：88

Parameter optimization method for random forest based on improved grid search algorithm
下载PDF
导出
摘要 随机森林是一种有效的集成学习算法,被广泛应用于模式识别中。为了得到更高的预测精度,需要对参数进行优化。提出了一种基于袋外数据估计的分类误差,利用改进的网格搜索算法对随机森林算法中的决策树数量和候选分裂属性数进行参数优化的随机森林算法。仿真结果表明,利用该方法优化得到的参数都能够使随机森林的分类效果得到一定程度的提高。 Random forest is an effective ensemble learning method,which is widely used in pattern recognition.In order to get higher accuracy,it is necessary to optimize the parameter of random forest.Based on generalization error of out-ofbag estimates,this paper proposes a parameter optimization method for a random forest with improved grid search.The parameter of the number of decision trees and candidate splitting attributes is optimized to improve accuracy.The simulation results demonstrates that optimized parameter by the method proposed in this paper makes the classification performance of random forest better.
作者 温博文 董文瀚 解武杰 马骏 WEN Bowen;DONG Wenhan;XIE Wujie;MA Jun(College of Aeronautics and Astronautics Engineering,Air Force Engineering University,Xi’an 710038,China)
机构地区 空军工程大学航空航天工程学院
出处 《计算机工程与应用》 CSCD 北大核心 2018年第10期154-157,共4页 Computer Engineering and Applications
关键词 随机森林 袋外估计 网格搜索 参数优化 random forest out-of-bag estimates grid search parameter optimization
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献3

二级参考文献44

  • 1SVETNIK A T, LIAW V, CULBERSON C. QSAR modelling using random forest, an ensemble learning tool for regression and classification[J]. Journal of Chemical Information and Computer Sciences, 2003, 43 (3) : 947-958.
  • 2RAICH A, CINAR A. Statistical process monitoring and disturbance diagnosis in multivariable continuous processes[J]. Aiche Journal, 1996, 42(4) :995-1009.
  • 3VENKATSUBRAMANIAN V, RENGASWAMY R, YIN K, et al. A review of process fault detection and diagnosis Part I: quantitative model-based methods[J]. Computers and Chemical Engineering, 2003, 27(3):293-311.
  • 4PALADE V, BOCANIALA C D, JAIN L C. Computational intelligence in fault diagnosis[M]. Berlin, Germany: Springer, 2006: 1-10.
  • 5V, RENGASWAMY R, KAVURI S N. A review of process fault detection and diagnosis Part III: Process history based methods[J]. Computers and Chemical Engineering, 2003, 27(3):327-346.
  • 6TZAFESTAS S G, DALIANIS P J. Fault diagnosis in complex systems using artificial neural networks[C]//Proceedings of the 3rd IEEE Conference on Control Applications. Washington, D. C. , USA:IEEE, 1994:877-882.
  • 7GE M, DU R, ZHANG G, et al. Fault diagnosis using support vector machine with an application in sheet metal stamping operations[J]. Mechanical Systems and Signal Processing, 2004, 18(1) :143-159.
  • 8HE Yigang, TAN Yanghong, SUN Yichuang. A neural network approach for fault diagnosis of large-scale analogue circuits[J]. IEEE International Symposium on Circuits and Systems, 2002, 1:153-156.
  • 9BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1) :5-32.
  • 10YAN Weizhong. Application of random forest to aircraft engine fault diagnosis[C]//Proceedings of IMACS Multiconferenee on Computational Engineering in Systems Applications. Washington, D.C., USA:IEEE, 2006:468-475.

共引文献49

同被引文献822

引证文献88

二级引证文献319

计算机工程与应用
2018年 第10期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部