Increasing the views and reducing the depth in random forest
作者：
Highlights：
• A novel approach, inspired from multi-view theory, for better classification.
• Suitable for high dimensional problems (with high number of features).
• Implemented on the random forest algorithm by increasing the number of trees.
• It is shown that the depth of trees can be limited without loss of accuracy.
• The method has been experimentally proven over several synthetic and real datasets.
摘要
•A novel approach, inspired from multi-view theory, for better classification.•Suitable for high dimensional problems (with high number of features).•Implemented on the random forest algorithm by increasing the number of trees.•It is shown that the depth of trees can be limited without loss of accuracy.•The method has been experimentally proven over several synthetic and real datasets.
论文关键词：Ensemble method,Random forest,Multi-view,High-dimensional data,Classification
论文评审过程：Received 7 April 2019, Revised 4 July 2019, Accepted 9 July 2019, Available online 10 July 2019, Version of Record 19 July 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.07.018
