Heterogeneous oblique random forest

Decision trees in random forests use a single feature in non-leaf nodes to split the data. Such splitting results in axis-parallel decision boundaries which may fail to exploit the geometric structure in the data. In oblique decision trees, an oblique hyperplane is employed instead of an axis-parallel hyperplane. Trees with such hyperplanes can better exploit the geometric structure to increase the accuracy of the trees and reduce the depth. The...