Automated Grammar-based Feature Selection in Symbolic Regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{ali:2022:GECCO,
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author = "Muhammad Sarmad Ali and Meghana Kshirsagar and
Enrique Naredo and Conor Ryan",
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title = "Automated Grammar-based Feature Selection in Symbolic
Regression",
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booktitle = "Proceedings of the 2022 Genetic and Evolutionary
Computation Conference",
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year = "2022",
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editor = "Alma Rahat and Jonathan Fieldsend and
Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and
Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and
Erik Hemberg and Christopher Cleghorn and Chao-li Sun and
Georgios Yannakakis and Nicolas Bredeche and
Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and
Sebastian Risi and Laetitia Jourdan and
Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and
John Woodward and Malcolm Heywood and Elizabeth Wanner and
Leonardo Trujillo and Domagoj Jakobovic and
Risto Miikkulainen and Bing Xue and Aneta Neumann and
Richard Allmendinger and Inmaculada Medina-Bulo and
Slim Bechikh and Andrew M. Sutton and
Pietro Simone Oliveto",
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pages = "902--910",
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address = "Boston, USA",
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series = "GECCO '22",
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month = "9-13 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, feature
selection, symbolic regression, production ranking,
grammatical evolution, grammar pruning",
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isbn13 = "978-1-4503-9237-2",
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DOI = "doi:10.1145/3512290.3528852",
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abstract = "With the growing popularity of machine learning (ML),
regression problems in many domains are becoming
increasingly high-dimensional. Identifying relevant
features from a high-dimensional dataset still remains
a significant challenge for building highly accurate
machine learning models.Evolutionary feature selection
has been used for high-dimensional symbolic regression
using Genetic Programming (GP). While grammar based GP,
especially Grammatical Evolution (GE), has been
extensively used for symbolic regression, no systematic
grammar-based feature selection approach exists. This
work presents a grammar-based feature selection method,
Production Ranking based Feature Selection (PRFS), and
reports on the results of its application in symbolic
regression.The main contribution of our work is to
demonstrate that the proposed method can not only
consistently select the most relevant features, but
also significantly improves the generalization
performance of GE when compared with several
state-of-the-art ML-based feature selection methods.
Experimental results on benchmark symbolic regression
problems show that the generalization performance of GE
using PRFS was significantly better than that of a
state-of-the-art Random Forest based feature selection
in three out of four problems, while in fourth problem
the performance was the same.",
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notes = "GECCO-2022 A Recombination of the 31st International
Conference on Genetic Algorithms (ICGA) and the 27th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Muhammad Sarmad Ali
Meghana Kshirsagar
Enrique Naredo
Conor Ryan
Citations