Automatic evolutionary learning of composite models with knowledge enrichment
Publication type: Proceedings Article
Publication date: 2020-07-08
Abstract
This paper provides the main concepts of the knowledge-enriched AutoML approach and shortly describes the current results of the proof of concept implementation within the FEDOT framework. By knowledge enrichment, we mean the insertion of domain-specific models and expert-like meta-heuristics. Also, we involve multi-scale learning as a part of complex models identification. The proposed concepts make it possible to create effective and interpretable composite models.
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Kalyuzhnaya A. V. et al. Automatic evolutionary learning of composite models with knowledge enrichment // GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. 2020. pp. 43-44.
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Kalyuzhnaya A. V., Nikitin N. O., Vychuzhanin P., Hvatov A., Boukhanovsky A. Automatic evolutionary learning of composite models with knowledge enrichment // GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. 2020. pp. 43-44.
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TY - CPAPER
DO - 10.1145/3377929.3398167
UR - https://doi.org/10.1145/3377929.3398167
TI - Automatic evolutionary learning of composite models with knowledge enrichment
T2 - GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
AU - Kalyuzhnaya, Anna V
AU - Nikitin, Nikolay O
AU - Vychuzhanin, Pavel
AU - Hvatov, Alexander
AU - Boukhanovsky, Alexander
PY - 2020
DA - 2020/07/08
PB - Association for Computing Machinery (ACM)
SP - 43-44
ER -
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@inproceedings{2020_Kalyuzhnaya,
author = {Anna V Kalyuzhnaya and Nikolay O Nikitin and Pavel Vychuzhanin and Alexander Hvatov and Alexander Boukhanovsky},
title = {Automatic evolutionary learning of composite models with knowledge enrichment},
year = {2020},
pages = {43--44},
month = {jul},
publisher = {Association for Computing Machinery (ACM)}
}