计算机工程与应用2018，Vol.54Issue(10)：154-157,4.DOI:10.3778/j.issn.1002-8331.1612-0328
基于改进网格搜索算法的随机森林参数优化
Parameter optimization method for random forest based on improved grid search algorithm
温博文 1董文瀚 1解武杰 1马骏1
作者信息
- 1. 空军工程大学 航空航天工程学院,西安710038
- 折叠
摘要
Abstract
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-of-bag 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 simula-tion results demonstrates that optimized parameter by the method proposed in this paper makes the classification perfor-mance of random forest better.关键词
随机森林/袋外估计/网格搜索/参数优化Key words
random forest/out-of-bag estimates/grid search/parameter optimization分类
信息技术与安全科学引用本文复制引用
温博文,董文瀚,解武杰,马骏..基于改进网格搜索算法的随机森林参数优化[J].计算机工程与应用,2018,54(10):154-157,4.
