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Proceedings Paper

On modified boosting algorithm for geographic data applications
Author(s): Michal Iwanowski; Jan Mulawka
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Paper Abstract

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.

Paper Details

Date Published: 11 September 2015
PDF: 7 pages
Proc. SPIE 9662, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2015, 96623L (11 September 2015); doi: 10.1117/12.2205625
Show Author Affiliations
Michal Iwanowski, Warsaw Univ. of Technology (Poland)
Jan Mulawka, Warsaw Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 9662:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2015
Ryszard S. Romaniuk, Editor(s)

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