A propositionalization method of multi-relational data based on Grammar-Guided Genetic Programming
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...
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Published in | Expert systems with applications Vol. 168; p. 114263 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
New York
Elsevier Ltd
15.04.2021
Elsevier BV |
Subjects | |
Online Access | Get full text |
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Summary: | 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.
•Grammar-Guided Genetic Programming is used to generate complex attributes.•Words coming from a Bag-of-Words representation are combined using Boolean operators.•Simple-instance and multiple-instance propositionalization were analyzed.•A considerable reduction in the dimensionality of the resulting datasets is achieved. |
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ISSN: | 0957-4174 1873-6793 |
DOI: | 10.1016/j.eswa.2020.114263 |