Multi-objective Genetic Programming for Multiple Instance Learning
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{DBLP:conf/ecml/ZafraV07,
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author = "Amelia Zafra and Sebastian Ventura",
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title = "Multi-objective Genetic Programming for Multiple
Instance Learning",
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booktitle = "18th European Conference on Machine Learning, ECML
2007",
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year = "2007",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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editor = "Joost N. Kok and Jacek Koronacki and
Ramon L{\'o}pez de M{\'a}ntaras and Stan Matwin and Dunja Mladenic and
Andrzej Skowron",
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publisher = "Springer",
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series = "Lecture Notes in Computer Science",
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volume = "4701",
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pages = "790--797",
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address = "Warsaw, Poland",
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month = sep # " 17-21",
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keywords = "genetic algorithms, genetic programming, poster",
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isbn13 = "978-3-540-74957-8",
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DOI = "doi:10.1007/978-3-540-74958-5_81",
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size = "8 pages",
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abstract = "This paper introduces the use of multi-objective
evolutionary algorithms in multiple instance learning.
In order to achieve this purpose, a multi-objective
grammar-guided genetic programming algorithm (MOG3P-MI)
has been designed. This algorithm has been evaluated
and compared to other existing multiple instance
learning algorithms. Research on the performance of our
algorithm is carried out on two well-known drug
activity prediction problems, Musk and Mutagenesis,
both problems being considered typical benchmarks in
multiple instance problems. Computational experiments
indicate that the application of the MOG3P-MI algorithm
improves accuracy and decreases computational cost with
respect to other techniques.",
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notes = "http://www.ecmlpkdd2007.org/poster_E.html",
- }
Genetic Programming entries for
Amelia Zafra Gomez
Sebastian Ventura
Citations