In this paper we present an empirical comparison of algorithm AdaBoost with its
modification called MadaBoost suitable for the boosting by filtering
framework. In the boosting by filtering one obtains an unweighted sample at
each stage that is randomly drawn from the current modified distribution in
contrast with the boosting by subsampling where one uses a weighted sample at
each stage. A boosting algorithm for the filtering framework might be very
useful in situations where we have a very large dataset and we want to reduce
the running time of the base learner using random sampling. AdaBoost was
originally designed for boosting by subsampling and it can be shown that is
not suitable for boosting by filtering since generating a new sample at each
iteration from the modified distribution might take exponential time. The
modification we propose is very simple, we just bound the weights at each step
using its initial value as a saturation bound so they weights cannot become
arbitrarily large as it happens in AdaBoost. We experimentally show that there
is not significant difference in accuracy compared with AdaBoost while it is
much more efficient in terms of running time in generating a sample from the
modified distribution at each step. This difference might be crucial when
trying to use boosting with a large dataset.