Improving random forests by neighborhood projection for effective text classification
作者：
Highlights：
• We propose a lazy version of the random forest classifier based on nearest neighbors.
• Our goal is to reduce overfitting due to very complex trees generated in noisy scenarios.
• We run a very extensive set of experiments covering hundreds of results in two domains.
• Our method was the best performer in almost all cases.
• Our method is also more scalable than other lazy solutions.
摘要
•We propose a lazy version of the random forest classifier based on nearest neighbors.•Our goal is to reduce overfitting due to very complex trees generated in noisy scenarios.•We run a very extensive set of experiments covering hundreds of results in two domains.•Our method was the best performer in almost all cases.•Our method is also more scalable than other lazy solutions.
论文关键词：Classification,Random forests,Lazy learning,Nearest neighbors,00-01,99-00
论文评审过程：Received 17 March 2018, Revised 24 May 2018, Accepted 26 May 2018, Available online 29 May 2018, Version of Record 15 June 2018.
论文官网地址：https://doi.org/10.1016/j.is.2018.05.006
