Double Random Forest

Double Random Forests (DRFs) are ensemble learning methods aiming to improve the accuracy and generalization ability of standard Random Forests by employing more sophisticated tree structures and splitting criteria. Current research focuses on incorporating oblique decision trees, which use linear combinations of features for splitting, rather than single features, to better capture complex data geometries. This is achieved through various techniques, including the use of multiple linear classifiers at each node and data transformations like Principal Component Analysis or Linear Discriminant Analysis. These advancements enhance DRF performance in diverse applications, particularly where data exhibits intricate relationships not easily captured by traditional axis-parallel splits.