Voting Combinations-Based Ensemble: A Hybrid Approach
Küçük Resim Yok
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In the field of Artificial Intelligence (AI), Machine Learning (ML) is a well-known and actively researched concept that assists to strengthen the accomplishment of classification results. The primary goal of this study is to categories and analyze ML and Ensemble Learning (EL) techniques. Six algorithms Bagging, C4.5 (J48), Stacking, Support Vector Machine (SVM), Naive Bayes (NB), and Boosting as well as the five UCI Datasets of ML Repository are being used to support this notion. These algorithms show the robustness and effectiveness of numerous approaches. To improve the performance, a voting-based ensemble classifier has been developed in this research along with two base learners (namely, Random Forest and Rotation Forest). Whereas important parameters have been taken into account for analytical processes, including: F-measure values, recall, precision, Area under Curve (Auc), and accuracy values. As a result, the main goal of this research is to improve binary classification and values by enhancing ML and EL approaches. We illustrate the experimental results that demonstrate the superiority of our model approach over well-known competing strategies. Image recognition and ML challenges, such as binary classification, can be solved using this method.
Açıklama
Anahtar Kelimeler
Artificial Intelligence, Data Mining, Classification, machine learning, Pattern Recognition
Kaynak
Celal Bayar Üniversitesi Fen Bilimleri Dergisi
WoS Q Değeri
Scopus Q Değeri
Cilt
18
Sayı
3
