Scopus
YÖKSİS DOI Eşleşti
SJR Q2
A novel feature selection using binary hybrid improved whale optimization algorithm
Journal of Supercomputing · Haziran 2023
YÖKSİS Kayıtları
A novel feature selection using binary hybrid improved whale optimization algorithm
JOURNAL OF SUPERCOMPUTING · 2023 SCI-Expanded
Doç. Dr. MUSTAFA SERTER UZER →
A novel feature selection using binary hybrid improved whale optimization algorithm
The Journal of Supercomputing · 2023 SCI
Dr. Öğr. Üyesi ONUR İNAN →
YÖKSİS Kayıtları — ISSN Eşleşmesi
A novel feature selection using binary hybrid improved whale optimization algorithm
2023 ISSN: 0920-8542 SCI Q2
Dr. Öğr. Üyesi ONUR İNAN →
A novel feature selection using binary hybrid improved whale optimization algorithm
2023 ISSN: 0920-8542 SCI-Expanded Q2
Doç. Dr. MUSTAFA SERTER UZER →
An alternative bounded distribution: regression model and applications
2024 ISSN: 0920-8542 SCI-Expanded Q2
Doç. Dr. KADİR KARAKAYA →
Makale Bilgileri
ISSN09208542
Yayın TarihiHaziran 2023
Cilt / Sayfa79 · 10020-10045
Scopus ID2-s2.0-85146955844
Özet
Some features in a dataset that contain irrelevant or unnecessary data may adversely affect both classification accuracy and the size of data. These negative effects are minimized by using feature selection (FS). Recently, researchers have tried to develop more effective methods by using swarm-based optimization methods in FS, apart from the usual FS methods used in data mining. In this study, a novel wrapper feature selection method based on binary hybrid optimization, called BWPLFS, consisting of a Whale Optimization Algorithm, Particle Swarm Optimization and Lévy Flight is proposed. Ten standard benchmark datasets from the UCI repository for performance evaluation of the proposed algorithm are employed and compared with other literature algorithms. Support vector machines are used both in the objective function of the proposed FS and for classification. The system created for feature selection and classification is run twenty times. As a result of these runs, the average of the fitness values, the average of the classification accuracies, the worst of the fitness values and the best of the fitness values, and the average number of the selected features are found. The BWPLFS is compared with methods in the literature in terms of these criteria. According to the results, it seems that the proposed method selects the most effective features and so it is very promising. In addition, by integrating the proposed algorithm with devices that provide decision support systems, it can be provided to produce more accurate and faster results.
Yazarlar (2)
1
Mustafa Serter Uzer
ORCID: 0000-0002-8829-5987
2
Onur Inan
Anahtar Kelimeler
Classification
Feature selection
Lévy flight
PSO
WOA
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Journal of Supercomputing
Q2
SJR Skoru0,729
H-Index99
YayıncıSpringer Netherlands
ÜlkeNetherlands
Hardware and Architecture (Q2)
Information Systems (Q2)
Software (Q2)
Theoretical Computer Science (Q2)
Metrikler
13
Atıf
2
Yazar
5
Anahtar Kelime