English
资源说明：In this paper, we examine a method for feature
subset selection based on Information
Theory. Initially, a framework for de ning
the theoretically optimal, but computationally
intractable, method for feature subset selection
is presented. We show that our goal
should be to eliminate a feature if it gives
us little or no additional information beyond
that subsumed by the remaining features. In
particular, this will be the case for both irrelevant
and redundant features. We then
give an ecient algorithm for feature selection
which computes an approximation to the
optimal feature selection criterion. The conditions
under which the approximate algorithm
is successful are examined. Empirical
results are given on a number of data sets,
showing that the algorithm e ectively handles
datasets with large numbers of features.
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