Multi-resolution Boosting for Classification and Regression Problems

Various fortes of boosting techniques have been popularly used in many data mining and machine learning related applications. Inspite of their great: success; boosting algorithms still suffer from a few open-ended problems that require closer investigation. The efficiency of any such ensemble technique significantly relies on the choice of the weak learners and the form of the loss function. In this paper, we propose a novel multi-resolution approach for choosing the weak learners during additive modeling. Our method applies insights front multi-resolution analysis and chooses the optimal learners at multiple resolutions during different, iterations of the boosting algorithms. We demonstrate the advantages of easing this novel framework for classification tasks and show results on different real-world datasets obtained from the UCI machine learning repository. Though demonstrated specifically in the context of boosting algorithms; our framework cart be easily accommodated in general additive modeling techniques.