Improving random forest algorithm by Lasso method. Issue 2 (22nd January 2021)

• Record Type: Journal Article
• Authors: Wang, Hui; Wang, Guizhi
• Is Part Of: Journal of statistical computation and simulation. Volume 91:Issue 2(2021)
• Page Start: 353
• Page End: 367
• Publication Date: 2021-01-22
• Subjects: random forest -- ensemble learning -- post-selection -- Lasso -- decision trees -- prediction; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2020.1814776
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : The random forest (RF) algorithm is a very practical and excellent ensemble learning algorithm. In this paper, we improve the random forest algorithm and propose an algorithm called 'post-selection boosting random forest' (PBRF). This algorithm combines the original random forest and the Lasso method, without giving the number of decision trees for final prediction in advance, it can dynamically obtain the decision trees according to different input samples to output the prediction results. Meanwhile, we verify that the proposed algorithm can improve the performance of the model through simulation studies and real data analysis.