Assessment of nature-inspired algorithms for text feature selection

• Autorzy: Çoban Önder

Identyfikatory

DOI

10.7494/csci.2022.23.2.4204

• Języki publikacji: EN

Abstrakty

EN

This paper provides a comprehensive assessment of basic feature selection (FS) methods that have originated from nature-inspired (NI) meta-heuristics; two well-known filter-based FS methods are also included for comparison. The performances of the considered methods are compared on four balanced highdimensional and real-world text data sets regarding the accuracy, the number of selected features, and computation time. This study differs from existing studies in terms of the extent of experimental analyses that were performed under different circumstances where the classifier, feature model, and term-weighting scheme were different. The results of the extensive experiments indicated that basic NI algorithms produce slightly different results than filter-based methods for the text FS problem. However, filter-based methods often provide better results by using lower numbers of features and computation times.

Słowa kluczowe

EN

nature-inspired algorithms; feature selection; text categorization

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2022
• Tom: T. 23 (2)
• Strony: 179--204
• Opis fizyczny: Bibliogr. 43 poz., rys., tab.

Twórcy

autor

Çoban Önder

• Adıyaman University, Department of Computer Engineering, Adıyaman, Turkey

• https://orcid.org/0000-0001-9404-2583

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).