PROCEEDINGS IPMU '08
On incremental wrapper-based attribute selection: experimental analysis of the relevance criteria
Pablo Bermejo, Jose A. Gámez, Jose M. Puerta
This paper deals with the problem
of feature subset selection in classification
oriented datasets with a
(very) large number of attributes.
In such datasets the classical wrapper
approaches become intractable
due to the high number of wrapper
evaluations to be carried out. One
way to alleviate this problem is to
use the so-called filter-wrapper approach,
which consists in the construction
of a ranking among the
predictive attributes by using a filter
measure, and then a wrapper
approach is used by following the
rank. In this way the number of
wrapper evaluations is linear with
the number of predictive attributes.
The main contribution of this paper
is the analysis of different relevance
criteria used to decide when a new
feature must be included or rejected
in the selected subset. Experiments
have been carried out with three different
criteria and different strictness
levels, and a statistical analysis
is used to draw the conclusions
about the best configurations to be
used.
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