Voting Combinations-Based Ensemble: A Hybrid Approach

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dc.contributor.authorAbro, Abdul Ahad
dc.contributor.authorTalpur, Dr. Mir Sajjad Hussain
dc.contributor.authorJumani, Awais Khan
dc.contributor.authorSıddıque, Waqas Ahmed
dc.contributor.authorYasar, Erkan
dc.date.accessioned2023-01-12T20:29:17Z
dc.date.available2023-01-12T20:29:17Z
dc.date.issued2022
dc.departmentN/A/Departmenten_US
dc.description.abstractIn 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.en_US
dc.identifier.doi10.18466/cbayarfbe.1014724
dc.identifier.endpage263en_US
dc.identifier.issn1305-130X
dc.identifier.issn1305-1385
dc.identifier.issue3en_US
dc.identifier.startpage257en_US
dc.identifier.trdizinid1127540en_US
dc.identifier.urihttps://doi.org/10.18466/cbayarfbe.1014724
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1127540
dc.identifier.urihttps://hdl.handle.net/11454/80415
dc.identifier.volume18en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofCelal Bayar Üniversitesi Fen Bilimleri Dergisien_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectData Miningen_US
dc.subjectClassificationen_US
dc.subjectmachine learningen_US
dc.subjectPattern Recognitionen_US
dc.titleVoting Combinations-Based Ensemble: A Hybrid Approachen_US
dc.typeArticleen_US

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