Caprile B., Furlanello C., Merler S.
The Dynamics of AdaBoost Weights Tells You What's Hard to Classify
ITC-irst, Technical report, June 2001, 7 pp.
Abstract The dynamical evolution of weights in the AdaBoost
algorithm contains useful information about the role that the
associated data points play in the built of the AdaBoost model. In
particular, the dynamics induces a bipartition of the data set into
two (easy/hard) classes. Easy points are ininfluential in the making
of the model, while the varying relevance of hard points can be gauged
in terms of an entropy value associated to their evolution. Smooth
approximations of entropy highlight regions where classification is
most uncertain. Promising results are obtained when methods proposed
are applied in the Optimal Sampling framework.