RolexBoost: A Rotation-Based Boosting Algorithm With Adaptive Loss Functions

We propose a new ensemble algorithm, called RolexBoost (Rotation-Flexible AdaBoost) that can not only secure diversity within an ensemble by rotating the feature axes in conjunction with performing the random subspace method for each bootstrap sample, but can also mitigate the impact of outlying data points by identifying the optimal loss function during the boosting iterations. The experimental results on 30 binary classification problems showed that RolexBoost yielded the best accuracy among the competing ensemble methods (AdaBoost, GentleBoost, Rotation Forest, Random Forest, Rotation Boost, FlexBoost). We statistically verified that the improvements made significant differences in the rankings. Additionally, we empirically verified that Rotation Boost is complemented by adaptive loss functions, demonstrating the superiority of RolexBoost.