research-article

Classification of Cardiac Arrhythmia by Random Forests with Features Constructed by Kaizen Programming with Linear Genetic Programming

Authors:

Léo F.D.P. Sotto,

Regina C. Coelho,

Vinícius V. de MeloAuthors Info & Claims

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

Pages 813 - 820

https://doi.org/10.1145/2908812.2908882

Published: 20 July 2016 Publication History

Abstract

Cardiac rhythm disorders may cause severe heart diseases, stroke, and even sudden cardiac death. Some arrhythmias are so serious that can cause injury to other organs, for instance, brain, kidneys, lungs or liver. Therefore, early and correct diagnosis of cardiac arrhythmia is essential to the prevention of serious problems. There are expert systems to classify arrhythmias from electrocardiograms signals. However, it has been shown that not only selecting the correct features from the dataset but also generating combined features could be the key to having real progress in classification. Therefore, this paper investigates a novel hybrid evolutionary technique to perform both tasks at the same time, finding complementary features that cover different characteristics of the data. The new features were tested with a widely-used classifier called Random Forests. The method reduced a dataset with 279 attributes to 26 attributes and achieved accuracies of 86.39% for binary classification and 77.69% for multiclass. Our approach outperformed several popular feature selection, feature generation, and state-of-the-art related work from the literature.

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Cited By

Agarwal IRana D(2021)Fake News and Imbalanced Data PerspectiveData Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance10.4018/978-1-7998-7371-6.ch011(195-210)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-7371-6.ch011
Iba HFeng JIzadi Rad H(2018)GP-RVM: Genetic Programing-Based Symbolic Regression Using Relevance Vector Machine2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2018.00054(255-262)Online publication date: Oct-2018
https://doi.org/10.1109/SMC.2018.00054
Nascimento Ade Melo VDias Lda Cunha A(2018)Increasing the Prediction Quality of Software Defective Modules with Automatic Feature EngineeringInformation Technology – New Generations10.1007/978-3-319-77028-4_68(527-535)Online publication date: 2018
https://doi.org/10.1007/978-3-319-77028-4_68

Index Terms

Classification of Cardiac Arrhythmia by Random Forests with Features Constructed by Kaizen Programming with Linear Genetic Programming
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Discrete space search

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Published In

cover image ACM Conferences

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

July 2016

1196 pages

ISBN:9781450342063

DOI:10.1145/2908812

Editor:
Tobias Friedrich
Hasso Plattner Institute
,
General Chair:
Frank Neumann
University of Adelaide
,
Program Chair:
Andrew M. Sutton
Hasso Plattner Institute

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2016

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Funding Sources

Brazilian Government CNPq (Universal)
São Paulo Research Foundation (FAPESP)
CAPES (Science without Borders program)

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GECCO '16

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SIGEVO

GECCO '16: Genetic and Evolutionary Computation Conference

July 20 - 24, 2016

Colorado, Denver, USA

Acceptance Rates

GECCO '16 Paper Acceptance Rate 137 of 381 submissions, 36%;

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

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Cited By

Agarwal IRana D(2021)Fake News and Imbalanced Data PerspectiveData Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance10.4018/978-1-7998-7371-6.ch011(195-210)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-7371-6.ch011
Iba HFeng JIzadi Rad H(2018)GP-RVM: Genetic Programing-Based Symbolic Regression Using Relevance Vector Machine2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2018.00054(255-262)Online publication date: Oct-2018
https://doi.org/10.1109/SMC.2018.00054
Nascimento Ade Melo VDias Lda Cunha A(2018)Increasing the Prediction Quality of Software Defective Modules with Automatic Feature EngineeringInformation Technology – New Generations10.1007/978-3-319-77028-4_68(527-535)Online publication date: 2018
https://doi.org/10.1007/978-3-319-77028-4_68

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