Abstract
In this paper, we present a generic, optimal feature
extraction method using multiobjective genetic
programming. We reexamine the feature extraction
problem and argue that effective feature extraction can
significantly enhance the performance of pattern
recognition systems with simple classifiers. A
framework is presented to evolve optimised feature
extractors that transform an input pattern space into a
decision space in which maximal class separability is
obtained. We have applied this method to real world
datasets from the UCI Machine Learning and StatLog
databases to verify our approach and compare our
proposed method with other reported results. We
conclude that our algorithm is able to produce
classifiers of superior (or equivalent) performance to
the conventional classifiers examined, suggesting
removal of the need to exhaustively evaluate a large
family of conventional classifiers on any new
problem.
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