Multi-objective approach based on grammar-guided genetic programming for solving multiple instance problems

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{journals/soco/ZafraV12,

• author = "Amelia Zafra and Sebastian Ventura",
• title = "Multi-objective approach based on grammar-guided genetic programming for solving multiple instance problems",
• journal = "Soft Computing - A Fusion of Foundations, Methodologies and Applications",
• year = "2012",
• number = "6",
• volume = "16",
• pages = "955--977",
• keywords = "genetic algorithms, genetic programming, multiple instance learning, multiple objective learning, grammar guided genetic programming, evolutionary rule learning",
• ISSN = "1432-7643",
• DOI = "doi:10.1007/s00500-011-0794-0",
• size = "23 pages",
• 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.",
• affiliation = "Department of Computer Science and Numerical Analysis, University of Cordoba, Cordoba, Spain",
• bibdate = "2012-05-14",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco16.html#ZafraV12",

}

Genetic Programming entries for Amelia Zafra Gomez Sebastian Ventura

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