In this paper, we examine the advantages and
disadvantages of filter and wrapper methods
for feature selection and propose a new hybrid
algorithm that uses boosting and incorporates
some of the features of wrapper
methods into a fast filter method for feature
selection. Empirical results are reported on
six real-world datasets from the UCI repository,
showing that our hybrid algorithm is
competitive with wrapper methods while being
much faster, and scales well to datasets
with thousands of features.