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Reducing thedimension of thefeature space for theclassificationproblem using agenetic programmingalgorithm Melnikova OlgaTausnev DaniilLipinskiy Leonid2022 год

Reducing the dimension of the feature space for the classification problem using a genetic programming algorithm : доклад, тезисы доклада
доклад, тезисы доклада, статья из сборника материалов конференций

Дополнительные ссылки: eLibrary · Источник статьи
База данных: Каталог библиотеки СФУ
Библиографическое описание: Reducing the dimension of the feature space for the classification problem using a genetic programming algorithm : доклад, тезисы доклада / O. Melnikova [et al.]. - [S. l. : s. n.], 2022. - Текст : непосредственный // Proceedings II International Scientific Conference on Advances in Science, Engineering and Digital Education (ASEDU-II-2021) : Conference Proceedings. - Krasnoyarsk : AIP PUBLISHING, 2022. - 2647, Issue 1. - P. 50030. - ISBN 9780735442382, DOI 10.1063/5.0106039.
Аннотация: Often, a decrease in the efficiency of machine learning algorithms is associated with the problem of classification quality on data with a large dimension of the feature space. Therefore, for data mining methods in solving classification problems, finding the optimal feature space is an important task. A method is proposed to dimensionality reduction of the feature space by constructing new features (a new feature space) using a genetic programming algorithm. The dimension of the space to which the source data needs to be compressed can be set at will. After dimensionality reduction, the accuracy of classification on the new feature space has almost minimal differences with respect to the accuracy of classification on the original feature space. In addition, the sample distribution of the original feature space is almost statistically not significantly different from the constructed one. The approbation of the obtained approach is presented on a representative set of test and practical tasks.
Год издания: 2022
Авторы: Melnikova Olga , Tausnev Daniil , Lipinskiy Leonid , Polyakova Anastasiya
Издательство: AIP PUBLISHING, Krasnoyarsk
Выпуск: 2647, Issue 1
Мероприятие: Proceedings II International Scientific Conference on Advances in Science, Engineering and Digital Education (ASEDU-II-2021) (Krasnoyarsk, 2021 ; 28.10 - 28.10)
Номера страниц: 50030
Держатель оригинала документа: Reshetnev Siberian State University of Science and Technology
Количество экземпляров:
Твёрдая копия издания отсутствует в фонде библиотеки

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