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Open access
Procedia Computer Science, volume 178, pages 414-423

Structural Evolutionary Learning for Composite Classification Models

Publication typeJournal Article
Publication date2020-12-07
Quartile SCImago
Quartile WOS
Impact factor
ISSN18770509
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.

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GOST Copy
Nikitin N. O. et al. Structural Evolutionary Learning for Composite Classification Models // Procedia Computer Science. 2020. Vol. 178. pp. 414-423.
GOST all authors (up to 50) Copy
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.
RIS |
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RIS Copy
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 -
BibTex
Cite this
BibTex Copy
@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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