Improved Boosted Regression Forests Through Non-Greedy Tree Optimization

Regression forests (ensembles of trees) are considered as the leading off-the-shelf method for regression. One of the main approaches of constructing such forests is based on boosting. However, majority of the current boosting implementations employ an axis-aligned tree as a base learner, where each decision node tests for a single feature. Moreover, such trees are usually trained by greedy top-down algorithms such as CART which is shown to be suboptimal. We instead use oblique trees, where each decision node tests for a linear combination of features and train them with the recently proposed non-greedy tree learning method-Tree Alternating Optimization (TAO). We embed the TAO algorithm into the boosting framework and show its effectiveness in the regression setting. We show that it produces much better forests than other types of tree ensembling methods in terms of error, model size and inference time. The result has an immense practical impact on various applications such as in signal processing, data mining, computer vision, etc.