Associative classification in big data through a G3P approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.7989
- @InProceedings{Luna:2019:IoTyBDS,
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author = "Jose Maria Luna and Francisco Padillo and
Sebastian Ventura",
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title = "Associative classification in big data through a {G3P}
approach",
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booktitle = "Proceedings of the 4th International Conference on
Internet of Things, Big Data and Security - IoTBDS",
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year = "2019",
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pages = "94--102",
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address = "Heraklion, Crete, Greece",
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publisher = "SciTePress",
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keywords = "genetic algorithms, genetic programming, Big Data
Mining, Evolutionary Algorithms, Grammar-Based Genetic
Programming, Pattern Mining",
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isbn13 = "978-989-758-369-8",
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URL = "http://doi.org/10.5220/0007688400940102",
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DOI = "doi:10.5220/0007688400940102",
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abstract = "The associative classification field includes really
interesting approaches for building reliable
classifiers and any of these approaches generally work
on four different phases (data discretization, pattern
mining, rule mining, and classifier building). This
number of phases is a handicap when big datasets are
analysed. The aim of this work is to propose a novel
evolutionary algorithm for efficiently building
associative classifiers in Big Data. The proposed model
works in only two phases (a grammar-guided genetic
programming framework is performed in each phase): 1)
mining reliable association rules; 2) building an
accurate classifier by ranking and combining the
previously mined rules. The proposal has been
implemented on Apache Spark to take advantage of the
distributed computing. The experimental analysis was
performed on 40 well-known datasets and considering 13
algorithms taken from literature. A series of
non-parametric tests has also been carried out to
determine statistical differences. Results are quite
promising in terms of reliability and efficiency on
high-dimensional data.",
- }
Genetic Programming entries for
Jose Maria Luna
Francisco Padillo
Sebastian Ventura
Citations