| A Generic multi-dimensional feature extraction method using multiobjective genetic programming. | |
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MedLine Citation:
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PMID: 19207089 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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In this paper, we present a generic feature extraction method for pattern classification using multiobjective genetic programming. This not only evolves the (near-)optimal set of mappings from a pattern space to a multi-dimensional decision space, but also simultaneously optimizes the dimensionality of that decision space. The presented framework evolves vector-to-vector feature extractors that maximize class separability. We demonstrate the efficacy of our approach by making statistically-founded comparisons with a wide variety of established classifier paradigms over a range of datasets and find that for most of the pairwise comparisons, our evolutionary method delivers statistically smaller misclassification errors. At very worst, our method displays no statistical difference in a few pairwise comparisons with established classifier/dataset combinations; crucially, none of the misclassification results produced by our method is worse than any comparator classifier. Although principally focused on feature extraction, feature selection is also performed as an implicit side effect; we show that both feature extraction and selection are important to the success of our technique. The presented method has the practical consequence of obviating the need to exhaustively evaluate a large family of conventional classifiers when faced with a new pattern recognition problem in order to attain a good classification accuracy. |
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Authors:
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Yang Zhang; Peter I Rockett |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: Evolutionary computation Volume: 17 ISSN: 1063-6560 ISO Abbreviation: Evol Comput Publication Date: 2009 |
Date Detail:
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Created Date: 2009-02-11 Completed Date: 2009-04-27 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9513581 Medline TA: Evol Comput Country: United States |
Other Details:
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Languages: eng Pagination: 89-115 Citation Subset: IM |
Affiliation:
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Laboratory for Image and Vision Engineering, Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, United Kingdom. hegallis@gmail.com |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Computers, Molecular Confidence Intervals Databases as Topic Humans Models, Genetic* Models, Statistical Models, Theoretical Mutation Reproducibility of Results Research / methods Research Design |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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