Implementation and performance op timization of dynamic random forest
Bernard combines the weight updating of the boosting algorithm with the Random Forest(RF),and proposes a new RF induction algorithm called Dynamic Random Forest(DRF).The idea with DRF is to grow only trees that would fit the sub-forest already built,use the existing forest to update the weight of each randomly selected training instance,force the next tree to pay attention to those samples that can not be classified well by the current forest,thus improving the RF accuracy.However,this weight updating method is still flawed,which does not make a good distinction between the samples classified correctly and the samples classified wrongly by the current forest.In this paper,we implement the DRF algorithm,and propose a new weight update method,that is,giving higher weight to the samples classified wrongly by the current forest,giving lower weight to the samples classified correctly by the current forest,so that the next tree will be more concerned with those misclassified samples.Experimental results show that our method is better than DRF algorithm and traditional RF algorithm.
Random forest Boosting Dynamic random forest Weight updating
Xiaolong Xu Wen Chen
School of Computer Science Nanjing University of Posts and Telecommunications Nanjing,China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and
国际会议
南京
英文
283-289
2017-10-12（万方平台首次上网日期，不代表论文的发表时间）