Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance

This paper proposes, for boosting, a novel method which prevents deterioration of accuracy inherent to data squashing methods. Boosting, which constructs a highly accurate classification model by combining multiple classification models, requires long computational time. Data squashing, which speeds-up a learning method by abstracting the training data set to a smaller data set, typically lowers accuracy. Our SB (Squashing-Boosting) loop, based on a distribution-sensitive distance, alternates data squashing and boosting, and iteratively refines an SF (Squashed-Feature) tree, which provides an appropriately squashed data set. Experimental evaluation with artificial data sets and the KDD Cup 1999 data set clearly shows superiority of our method compared with conventional methods. We have also empirically evaluated our distance measure as well as our SF tree, and found them superior to alternatives.