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Semi-supervised datamining tool designwith self-tuningoptimizationtechniques : научноеиздание Akhmedova S.Semenkina M.Stanovov V.2020 год

Semi-supervised data mining tool design with self-tuning optimization techniques : научное издание
статья из журнала

Дополнительные ссылки: РИНЦ
База данных: Каталог библиотеки СФУ
Библиографическое описание: Semi-supervised data mining tool design with self-tuning optimization techniques : научное издание / S. Akhmedova [et al.]. - Текст : непосредственный // Lecture Notes in Electrical Engineering. - 2020. - Т. 495, № . - P. 87-105. - ISSN 1876-1100. - ISSN 1876-1119, DOI 10.1007/978-3-030-11292-9_5.
Аннотация: Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Presented in this article are semi-supervised artificial neural network- (ANN) and support vector machine- (SVM) based classifiers designed by the self-configuring genetic algorithm (SelfCGA) and the fuzzy controlled meta-heuristic approach Co-operation of Biology Related Algorithms (COBRA). Both data mining tools are based on dividing instances from different classes using both labelled and unlabelled examples. A new collective bionic algorithm, namely fuzzy controlled cooperation of biology-related algorithms, which solves constrained optimization problems, COBRA-cf, has been developed for the design of semi-supervised SVMs. Firstly, the experimental results obtained by the two types of fuzzy controlled COBRA are presented and compared and their usefulness is demonstrated. Then the performance and behaviour of the proposed semi-supervised SVMs and semi-supervised ANNs were studied under common experimental settings and their workability was established. Then their efficiency was estimated on a speech-based emotion recognition problem. Thus, the workability of the proposed meta-heuristic optimization algorithms was confirmed.
Год издания: 2020
Авторы: Akhmedova S. , Semenkina M. , Stanovov V. , Semenkin E.
Источник: Lecture Notes in Electrical Engineering
Выпуск: Т. 495
Номера страниц: 87-105
Держатель оригинала документа: Reshetnev Siberian State University of Science and Technology
Количество экземпляров:
Твёрдая копия издания отсутствует в фонде библиотеки

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