Hybrid Sampling Method For Autoregressive Classification Trees Under Density-Weighted Curvature Distance

To improve human action recognition performance of visual data, we proposed Hybrid Sampling Ensemble Learning method. 1)define the curvature distance distribution to fit discrete images into continuous expandable surfaces. 2)obtain density-weighted curvature distance of the images through class imbalance adjustment. 3)use auto-regressive classification tree strategy by utilizing the hierarchical regress of the filtered images. The classification performance of the ensemble learning method is not as good as the deep learning framework. However, the parameters are interpretable, and the construction of the classification framework is simple. In short, the proposed ensemble model is robust and controllable.