Multi-objective approach based on grammar-guided genetic programming for solving multiple instance problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{journals/soco/ZafraV12,
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author = "Amelia Zafra and Sebastian Ventura",
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title = "Multi-objective approach based on grammar-guided
genetic programming for solving multiple instance
problems",
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journal = "Soft Computing - A Fusion of Foundations,
Methodologies and Applications",
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year = "2012",
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number = "6",
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volume = "16",
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pages = "955--977",
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keywords = "genetic algorithms, genetic programming, multiple
instance learning, multiple objective learning, grammar
guided genetic programming, evolutionary rule
learning",
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ISSN = "1432-7643",
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DOI = "doi:10.1007/s00500-011-0794-0",
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size = "23 pages",
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abstract = "Multiple instance learning (MIL) is considered a
generalisation of traditional supervised learning which
deals with uncertainty in the information. Together
with the fact that, as in any other learning framework,
the classifier performance evaluation maintains a
trade-off relationship between different conflicting
objectives, this makes the classification task less
straightforward. This paper introduces a
multi-objective proposal that works in a MIL scenario
to obtain well-distributed Pareto solutions to
multi-instance problems. The algorithm developed,
Multi-Objective Grammar Guided Genetic Programming for
Multiple Instances (MOG3P-MI), is based on
grammar-guided genetic programming, which is a robust
tool for classification. Thus, this proposal combines
the advantages of the grammar-guided genetic
programming with benefits provided by multi-objective
approaches. First, a study of multi-objective
optimisation for MIL is carried out. To do this, three
different extensions of MOG3P-MI are designed and
implemented and their performance is compared. This
study allows us on the one hand, to check the
performance of multi-objective techniques in this
learning paradigm and on the other hand, to determine
the most appropriate evolutionary process for MOG3P-MI.
Then, MOG3P-MI is compared with some of the most
significant proposals developed throughout the years in
MIL. Computational experiments show that MOG3P-MI often
obtains consistently better results than the other
algorithms, achieving the most accurate models.
Moreover, the classifiers obtained are very
comprehensible.",
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affiliation = "Department of Computer Science and Numerical Analysis,
University of Cordoba, Cordoba, Spain",
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bibdate = "2012-05-14",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/soco/soco16.html#ZafraV12",
- }
Genetic Programming entries for
Amelia Zafra Gomez
Sebastian Ventura
Citations