A Grammar-Guided Genetic Programing Algorithm for Associative Classification in Big Data
Created by W.Langdon from
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- @Article{Padillo:2019:CC,
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author = "Francisco Padillo and Jose Maria Luna and
Sebastian Ventura",
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title = "A Grammar-Guided Genetic Programing Algorithm for
Associative Classification in Big Data",
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journal = "Cognitive Computation",
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year = "2019",
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volume = "11",
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issue = "3",
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pages = "331--346",
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keywords = "genetic algorithms, genetic programming, Big Data
Mining, Evolutionary Algorithms, Grammar-Based Genetic
Programming, Pattern Mining",
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ISSN = "1866-9964",
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URL = "https://link.springer.com/article/10.1007/s12559-018-9617-2",
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DOI = "doi:10.1007/s12559-018-9617-2",
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abstract = "The state-of-the-art in associative classification
includes interesting approaches for building accurate
and interpretable classifiers. These approaches
generally work on four different phases (data
discretization, pattern mining, rule mining, and
classifier building), some of them being computational
expensive. 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 different architectures (multi-thread,
Apache Spark and Apache Flink) to take advantage of the
distributed computing. The experimental results have
been obtained on 40 well-known datasets and analyzed
through non-parametric tests. Results were compared to
multiple approaches in the field and analyzed on three
ways: quality of the predictions, level of
interpretability, and efficiency. The proposed method
obtained accurate and interpretable classifiers in an
efficient way even on high-dimensional data,
outperforming the state-of-the-art algorithms on three
different levels: quality of the predictions,
interpretability, and efficiency.",
- }
Genetic Programming entries for
Francisco Padillo
Jose Maria Luna
Sebastian Ventura
Citations