A Generic Optimal Feature Extraction Method using Multiobjective Genetic Programming
Created by W.Langdon from
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- @TechReport{VIE2006-002,
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author = "Yang Zhang and Peter I. Rockett",
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title = "A Generic Optimal Feature Extraction Method using
Multiobjective Genetic Programming",
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institution = "Department of Electronic and Electrical Engineering,
University of Sheffield",
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year = "2006",
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number = "VIE 2006/001",
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address = "UK",
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keywords = "genetic algorithms, genetic programming, Feature
Extraction, Multiobjective Optimisation, MOGP, Pattern
Recognition",
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URL = "http://www.shef.ac.uk/eee/vie/tech/VIE2006-002.pdf",
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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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size = "29 pages",
- }
Genetic Programming entries for
Yang Zhang
Peter I Rockett
Citations