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Application ofNatural LanguageProcessing andGenetic Algorithm toFine-TuneHyperparameters ofClassifiers forEconomic ActivitiesAnalysis Malashin IvanMasich IgorTynchenko Vadim2024 год

Application of Natural Language Processing and Genetic Algorithm to Fine-Tune Hyperparameters of Classifiers for Economic Activities Analysis
статья из журнала

Дополнительные ссылки: eLibrary
База данных: Каталог библиотеки СФУ
Библиографическое описание: Application of Natural Language Processing and Genetic Algorithm to Fine-Tune Hyperparameters of Classifiers for Economic Activities Analysis : научное издание / I. I. Malashin [et al.]. - Текст : непосредственный // Big Data and Cognitive Computing. - 2024. - Т. 8, № 6. - P. 68. - ISSN 2504-2289, DOI 10.3390/bdcc8060068.
Аннотация: <jats:p>This study proposes a method for classifying economic activity descriptors to match Nomenclature of Economic Activities (NACE) codes, employing a blend of machine learning techniques and expert evaluation. By leveraging natural language processing (NLP) methods to vectorize activity descriptors and utilizing genetic algorithm (GA) optimization to fine-tune hyperparameters in multi-class classifiers like Naive Bayes, Decision Trees, Random Forests, and Multilayer Perceptrons, our aim is to boost the accuracy and reliability of an economic classification system. This system faces challenges due to the absence of precise target labels in the dataset. Hence, it is essential to initially check the accuracy of utilized methods based on expert evaluations using a small dataset before generalizing to a larger one.</jats:p>
Год издания: 2024
Авторы: Malashin Ivan , Masich Igor , Tynchenko Vadim , Nelyub Vladimir , Borodulin Aleksei , Gantimurov Andrei
Издательство: MDPI AG
Источник: Big Data and Cognitive Computing
Выпуск: Т. 8, № 6
Номера страниц: 68
Держатель оригинала документа: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, Moscow 105005, Russia
Количество экземпляров:
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