On modified boosting algorithm for geographic data applications
Michal Iwanowski, JAN J. MULAWKA · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
Boosting algorithms constitute one of the essential tools in modern machine-learning, one of its primary applications being the improvement of classifier accuracy in supervised learning. Most widespread realization of boosting, known as AdaBoost, is based upon the concept of building a complex predictive model out of a group of simple base models. We present an approach for local assessment of base model accuracy and their improved weighting that captures inhomogeneity present in real-life datasets, in particular in those that contain geographic information. Conducted experiments show improvement in classification accuracy and F-scores of the modified algorithm, however more experimentation is required to confirm the exact scope of these improvements.