A Comparison of Multi-objective Grammar-Guided Genetic Programming Methods to Multiple Instance Learning
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- @InProceedings{conf/hais/ZafraV09,
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title = "A Comparison of Multi-objective Grammar-Guided Genetic
Programming Methods to Multiple Instance Learning",
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author = "Amelia Zafra and Sebastian Ventura",
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booktitle = "Proceedings of the 4th International Conference on
Hybrid Artificial Intelligence Systems, HAIS 2009",
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year = "2009",
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editor = "Emilio Corchado and Xindong Wu and Erkki Oja and
{\'A}lvaro Herrero and Bruno Baruque",
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volume = "5572",
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series = "Lecture Notes in Computer Science",
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pages = "450--458",
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address = "Salamanca, Spain",
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month = jun # " 10-12",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-02318-7",
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DOI = "doi:10.1007/978-3-642-02319-4_54",
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bibdate = "2009-06-24",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/hais/hais2009.html#ZafraV09",
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abstract = "This paper develops a first comparative study of
multiobjective algorithms in Multiple Instance Learning
(MIL) applications. These algorithms use grammar-guided
genetic programming, a robust classification paradigm
which is able to generate understandable rules that are
adapted to work with the MIL framework. The algorithms
obtained are based on the most widely used and compared
multi-objective evolutionary algorithms. Thus, we
design and implement SPG3P-MI based on the Strength
Pareto Evolutionary Algorithm, NSG3P-MI based on the
Non-dominated Sorting Genetic Algorithm and MOGLG3P-MI
based on the Multi-objective genetic local search.
These approaches are tested with different MIL
applications and compared to a previous singleobjective
grammar-guided genetic programming proposal. The
results demonstrate the excellent performance of
multi-objective approaches in achieving accurate models
and their ability to generate comprehensive rules in
the knowledgable discovery process.",
- }
Genetic Programming entries for
Amelia Zafra Gomez
Sebastian Ventura
Citations