- author = "Nikolay O. Nikitin and Iana S. Polonskaia and Pavel Vychuzhanin and Irina V. Barabanova and Anna V. Kalyuzhnaya",
- title = "Structural Evolutionary Learning for Composite Classification Models",
- journal = "Procedia Computer Science",
- year = "2020",
- volume = "178",
- pages = "414--423",
- note = "special issue: 9th International Young Scientists Conference in Computational Science, YSC2020, 05-12 September 2020",
- keywords = "genetic algorithms, genetic programming, evolutionary learning, data-driven models, AutoML, FEDOT, machine learning, classification",
- ISSN = "1877-0509",
-
URL = "
http://www.human-competitive.org/sites/default/files/nikitin_human_competitive.txt",
-
URL = "
http://www.human-competitive.org/sites/default/files/structural_evolutionary_learning_for_composite_classification_models.pdf",
-
URL = "https://www.sciencedirect.com/science/article/pii/S1877050920324224",
-
DOI = "
doi:10.1016/j.procs.2020.11.043",
-
code_url = "https://github.com/ITMO-NSS-team/FEDOT.Algs",
- size = "10 pages",
- abstract = "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.",
-
notes = "Entered 2021 HUMIES see
\cite{Kalyuzhnaya:2020:GECCOcomp}
ITMO University, 49 Kronverksky Pr. St. Petersburg, 197101, Russian Federation",
