A propositionalization method of multi-relational data based on Grammar-Guided Genetic Programming

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@Article{QUINTERODOMINGUEZ:2021:ESA,

• author = "Luis A. Quintero-Dominguez and Carlos Morell and Sebastian Ventura",
• title = "A propositionalization method of multi-relational data based on Grammar-Guided Genetic Programming",
• journal = "Expert Systems with Applications",
• volume = "168",
• pages = "114263",
• year = "2021",
• ISSN = "0957-4174",
• DOI = "doi:10.1016/j.eswa.2020.114263",
• URL = "https://www.sciencedirect.com/science/article/pii/S095741742030974X",
• keywords = "genetic algorithms, genetic programming, G3Prop, Multi-Relational Data Mining, Propositionalization, Bag-of-Words, Grammar-Guided Genetic Programming",
• 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