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Evolutionary learning of meta-rules for text classification

Published: 15 July 2017 Publication History

Abstract

This paper presents an evolutionary method for learning lists of meta-rules for generalizing the selection of the best classifier for a given text dataset. The method builds rules based on features of a set of training text datasets, and evolves them using special crossover and mutation operators. Once the rules are learned, they are tested in a different set of datasets to demonstrate their accuracy and generality. Our experiments show encouraging results.

References

[1]
E. R. Sparks et al. 2015. Automating model search for large scale machine learning. In Proceedings of the Sixth ACM Symposium on Cloud Computing. ACM, 368--380.
[2]
I. Guyon et al. 2016. A brief review of the ChaLearn AutoML challenge: Anytime any-dataset learning without human intervention. In Proceedings of the 2016 Workshop on Automatic Machine Learning. 21--30.
[3]
J. C. Gomez and H. Terashima-Marin. 2012. Building general hyper-heuristics for multi-objective cutting stock problems. Computación y Sistemas 16, 3 (2012), 321--334.

Cited By

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  • (2023)AutoNLP: A Framework for Automated Model Selection in Natural Language Processing2023 18th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI58278.2023.10212030(1-4)Online publication date: 20-Jun-2023
  • (2023)Evolutionary learning of selection hyper-heuristics for text classificationApplied Soft Computing10.1016/j.asoc.2023.110721(110721)Online publication date: Aug-2023
  • (2021)Predicting Vaccine Hesitancy and Vaccine Sentiment Using Topic Modeling and Evolutionary OptimizationNatural Language Processing and Information Systems10.1007/978-3-030-80599-9_23(255-263)Online publication date: 20-Jun-2021
  • Show More Cited By

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Published In

cover image ACM Conferences
GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2017
1934 pages
ISBN:9781450349390
DOI:10.1145/3067695
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 July 2017

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Author Tags

  1. automatic machine learning
  2. genetic algorithms
  3. hyper-heuristics
  4. text classification

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2023)AutoNLP: A Framework for Automated Model Selection in Natural Language Processing2023 18th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI58278.2023.10212030(1-4)Online publication date: 20-Jun-2023
  • (2023)Evolutionary learning of selection hyper-heuristics for text classificationApplied Soft Computing10.1016/j.asoc.2023.110721(110721)Online publication date: Aug-2023
  • (2021)Predicting Vaccine Hesitancy and Vaccine Sentiment Using Topic Modeling and Evolutionary OptimizationNatural Language Processing and Information Systems10.1007/978-3-030-80599-9_23(255-263)Online publication date: 20-Jun-2021
  • (2020)Meta-learning of Textual RepresentationsMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-43823-4_6(57-67)Online publication date: 28-Mar-2020
  • (2019)Meta-learning of Text Classification TasksProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-030-33904-3_10(107-119)Online publication date: 22-Oct-2019

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