A propositionalization method of multi-relational data based on Grammar-Guided Genetic Programming
Created by W.Langdon from
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- @Article{QUINTERODOMINGUEZ:2021:ESA,
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author = "Luis A. Quintero-Dominguez and Carlos Morell and
Sebastian Ventura",
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title = "A propositionalization method of multi-relational data
based on Grammar-Guided Genetic Programming",
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journal = "Expert Systems with Applications",
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volume = "168",
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pages = "114263",
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year = "2021",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2020.114263",
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URL = "https://www.sciencedirect.com/science/article/pii/S095741742030974X",
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keywords = "genetic algorithms, genetic programming, G3Prop,
Multi-Relational Data Mining, Propositionalization,
Bag-of-Words, Grammar-Guided Genetic Programming",
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abstract = "The propositionalization process tries to find
distinctive features of the examples in a database to
transform such relational data into a simpler
representation. More informative features have a
positive impact on the classification capabilities of
the learning algorithms. In this work, we propose a new
propositionalization method, which generates complex
Boolean attributes using Grammar-Guided Genetic
Programming (G3P). The generated attributes are
compound formulas that combine word items coming from a
Bag-of-Words (BoW) representation using Boolean
operators. The proposal was assessed against three
state-of-the-art simple-instance and multiple-instance
propositionalization methods. The experimental results
show that the proposed method achieves an improvement
in terms of classification accuracy and a considerable
reduction in the dimensionality of the resulting
datasets",
- }
Genetic Programming entries for
Luis A Quintero-Dominguez
Carlos Morell
Sebastian Ventura
Citations