POPRAWA PARAMETRÓW REGRESJI WEKTORA NOŚNEGO V Z RÓWNOLEGŁYM WYBOREM CECHY POPRZEZ WYKORZYSTANIE ALGORYTMU QUASI-OPOZYCYJNEGO I ALGORYTMU OPTYMALIZACJI HARRIS HAWKS
Omar Mohammed Ismael
Ministry of Education, Directorate of Education in Nineveh (Irak)
https://orcid.org/0009-0005-6739-4790
Omar Saber Qasim
omar.saber@uomosul.edu.iqUniversity of Mosul, Department of Mathematics (Irak)
https://orcid.org/0000-0003-3301-6271
Zakariya Yahya Algamal
University of Mosul, Department of Statistics and Informatics (Irak)
https://orcid.org/0000-0002-0229-7958
Abstrakt
Liczne problemy występujące w świecie rzeczywistym rozwiązano za pomocą regresji wektora nośnego, w szczególności regresji wektora nośnego v (v-SVR), ale niektóre parametry wymagają ręcznej zmiany. Ponadto v-SVR nie obsługuje wyboru funkcji. Do identyfikacji cech i estymacji hiperparametrów wykorzystano techniki inspirowane naturą. W tym badaniu wprowadzono quasi-opozycyjną metodę optymalizacji Harris Hawks (QOBL-HHOA), aby osadzić selekcję cech i jednocześnie optymalizować hiperparametr v-SVR. Wyniki eksperymentów przeprowadzono przy użyciu czterech zbiorów danych. Wykazano, że pod względem predykcji, liczby możliwych do wybrania cech oraz czasu wykonania zaproponowany algorytm sprawdza się lepiej niż metody krzyżowej walidacji i wyszukiwania siatki. W porównaniu z innymi algorytmami inspirowanymi naturą wyniki eksperymentalne QOBL-HHOA pokazują jego skuteczność w poprawianiu dokładności przewidywań i czasu przetwarzania. Wykazuje również zdolność QOBL. Wyszukując optymalne wartości hiperparametrów, HHOA mogą zlokalizować funkcje, które są najbardziej przydatne w zadaniach predykcyjnych. W rezultacie algorytm QOBL-HHOA może być bardziej odpowiedni niż inne algorytmy do identyfikacji łącza danych pomiędzy cechami wejścia a pożądaną zmienną. Natomiast wyniki numeryczne wykazały wyższość tej metody nad wymienionymi metodami, na przykład błąd średniokwadratowy wyników metody QOBL-HHOA (2,05E-07) z zestawem danych dotyczących neuraminidazy grypy był lepszy niż w pozostałych. Jest to niezwykle pomocne przy przewidywaniu innych sytuacji w świecie rzeczywistym.
Słowa kluczowe:
regresja wektora v-nośnego, algorytm Harris hawks, wybór hiperparametrów, uczenie się quasi-opozycyjneBibliografia
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Autorzy
Omar Mohammed IsmaelMinistry of Education, Directorate of Education in Nineveh Irak
https://orcid.org/0009-0005-6739-4790
Autorzy
Omar Saber Qasimomar.saber@uomosul.edu.iq
University of Mosul, Department of Mathematics Irak
https://orcid.org/0000-0003-3301-6271
Autorzy
Zakariya Yahya AlgamalUniversity of Mosul, Department of Statistics and Informatics Irak
https://orcid.org/0000-0002-0229-7958
Statystyki
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