Abstract
This paper presents an online feature selection
algorithm using genetic programming (GP). The proposed
GP methodology simultaneously selects a good subset of
features and constructs a classifier using the selected
features. For a c-class problem, it provides a
classifier having c trees. In this context, we
introduce two new crossover operations to suit the
feature selection process. As a byproduct, our
algorithm produces a feature ranking scheme. We tested
our method on several data sets having dimensions
varying from 4 to 7129. We compared the performance of
our method with results available in the literature and
found that the proposed method produces consistently
good results. To demonstrate the robustness of the
scheme, we studied its effectiveness on data sets with
known (synthetically added) redundant/bad features.
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