Boosting Localized Classifiers in Heterogeneous Databases
Aleksandar M Lazarević, Zoran Obradović · 2001
Combining multiple global models (e.g. back-propagation based neural networks) is an effective technique for improving classification accuracy. This technique reduces variance by manipulating the distribution of the training data. In many large scale data analysis problems involving heterogeneous databases with attribute instability, standard boosting methods can be improved by coalescing multiple classifiers. Each classifier uses different germane attribute information that is identified through the attribute selection process. We propose a new technique of boosting localized classifiers when heterogeneous data sets contain more homogeneous data distributions. Instead of a single global classifier for each boosting round, we have localized classifiers responsible for each homogeneous region. The number of regions is identified through a clustering algorithm performed at each boosting iteration. A new boosting method applied to real life spatial data and synthetic spatial data shows improvements in prediction accuracy when unstable driving attributes and heterogeneity are present in the data. In addition, boosting localized experts significantly reduces the number of iterations needed for achieving the maximal prediction accuracy.