Improved Two-View Random Forest

Abstract

Random forest (RF) is one of the most classic machine learning methods, which has been widely used. However, although there are many two-view data in reality and extensive analytical research has been carried out, the RF construction for two-view scenarios is little. The only RF method for two-view learning first generates RF for each view respectively, and then merges the view information when making decisions. Therefore, it turns out an obvious disadvantage that the correlation between views is not utilized effectively during the RF construction stage, which undoubtedly wastes information resources. In order to make up for this disadvantage, an improved two-view RF (ITVRF) is proposed in this paper. Specifically, canonical correlation analysis (CCA) is used for view fusion in the process of generating decision trees, and the information interaction between views is embedded into the tree construction stage, realizing the utilization of complementary information between views in the entire RF generation process. In addition, ITVRF also generates discriminant decision boundaries for decision trees through discriminant analysis and thus makes it more suitable for classification. Experimental results show that ITVRF achieves better accuracy than existing two-view RF (TVRF).

Keywords

• |decision tree|random forest (rf)|two-view learning|canonical correlation analysis (cca)