Open Access
Procedia Computer Science, volume 178, pages 414-423
Structural Evolutionary Learning for Composite Classification Models
Publication type: Journal Article
Publication date: 2020-12-07
Journal:
Procedia Computer Science
Quartile SCImago
— Quartile WOS
—
Impact factor: —
ISSN: 18770509
General Medicine
Abstract
In this paper, we propose an evolutionary learning approach for flexible identification of custom composite models for classification problems. To solve this problem in an efficient way, the problem-specific evolutionary operators are proposed and the effectiveness of different modifications of the common genetic programming algorithm is investigated. Also, several implementations of caching for the fitted models were compared from the performance point of view. To verify the proposed algorithm, both synthetic and real-world classification cases are examined. The implemented solution can identify the structure of the composite models from scratch, as well as be used as a part of automated machine learning solutions.
Citations by journals
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3
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Procedia Computer Science
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Procedia Computer Science
3 publications, 30%
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Future Generation Computer Systems
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Future Generation Computer Systems
1 publication, 10%
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Entropy
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Entropy
1 publication, 10%
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Communications in Computer and Information Science
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Communications in Computer and Information Science
1 publication, 10%
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Heliyon
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Heliyon
1 publication, 10%
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Lecture Notes in Computer Science
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Lecture Notes in Computer Science
1 publication, 10%
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Advances in Intelligent Systems and Computing
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Advances in Intelligent Systems and Computing
1 publication, 10%
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3
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Citations by publishers
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5
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Elsevier
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Elsevier
5 publications, 50%
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Springer Nature
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Springer Nature
3 publications, 30%
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Multidisciplinary Digital Publishing Institute (MDPI)
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Multidisciplinary Digital Publishing Institute (MDPI)
1 publication, 10%
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- We do not take into account publications that without a DOI.
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- Statistics recalculated weekly.
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Nikitin N. O. et al. Structural Evolutionary Learning for Composite Classification Models // Procedia Computer Science. 2020. Vol. 178. pp. 414-423.
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Nikitin N. O., Polonskaia I. S., Vychuzhanin P., Barabanova I. V., Kalyuzhnaya A. V., Barabanova I. V., Kalyuzhnaya A. Structural Evolutionary Learning for Composite Classification Models // Procedia Computer Science. 2020. Vol. 178. pp. 414-423.
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TY - JOUR
DO - 10.1016/j.procs.2020.11.043
UR - https://doi.org/10.1016%2Fj.procs.2020.11.043
TI - Structural Evolutionary Learning for Composite Classification Models
T2 - Procedia Computer Science
AU - Nikitin, Nikolay O
AU - Polonskaia, Iana S
AU - Vychuzhanin, Pavel
AU - Barabanova, Irina V
AU - Kalyuzhnaya, Anna V
AU - Barabanova, Irina V
AU - Kalyuzhnaya, Anna
PY - 2020
DA - 2020/12/07 00:00:00
PB - Elsevier
SP - 414-423
VL - 178
SN - 1877-0509
ER -
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@article{2020_Nikitin
author = {Nikolay O Nikitin and Iana S Polonskaia and Pavel Vychuzhanin and Irina V Barabanova and Anna V Kalyuzhnaya and Irina V Barabanova and Anna Kalyuzhnaya},
title = {Structural Evolutionary Learning for Composite Classification Models},
journal = {Procedia Computer Science},
year = {2020},
volume = {178},
publisher = {Elsevier},
month = {dec},
url = {https://doi.org/10.1016%2Fj.procs.2020.11.043},
pages = {414--423},
doi = {10.1016/j.procs.2020.11.043}
}
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