A generic optimising feature extraction method using multiobjective genetic programming
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- @Article{Zhang20111087,
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author = "Yang Zhang and Peter I. Rockett",
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title = "A generic optimising feature extraction method using
multiobjective genetic programming",
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journal = "Applied Soft Computing",
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year = "2011",
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volume = "11",
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number = "1",
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pages = "1087--1097",
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month = jan,
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keywords = "genetic algorithms, genetic programming, Feature
extraction, Multiobjective optimisation, Pattern
recognition",
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ISSN = "1568-4946",
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broken = "http://www.sciencedirect.com/science/article/B6W86-4YGHGKT-2/2/3c6f14d2e029af14747957a5a2ccfd11",
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DOI = "doi:10.1016/j.asoc.2010.02.008",
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abstract = "In this paper, we present a generic, optimising
feature extraction method using multiobjective genetic
programming. We re-examine the feature extraction
problem and show 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.",
- }
Genetic Programming entries for
Yang Zhang
Peter I Rockett
Citations