Kılıç, UmitKaya Keles, Mumine2025-01-062025-01-062018978-1-5386-7786-5https://hdl.handle.net/20.500.14669/1880Innovations in Intelligent Systems and Applications Conference (ASYU) -- OCT 04-06, 2018 -- Adana, TURKEYRelevant and irrelevant features compose data. Evaluation of these features is the fundamental task for classification and clustering processes and during this processes, irrelevant features induce obtaining false results. Likewise, due to the relevant features' direct effect on the processes, results can be more correct and stable. This also represents the aim of the feature selection process that tries to achieve as high as possible results with as small as possible feature selection subset. In this study, Artificial Bee Colony (ABC) algorithm based feature selection method is updated and employed on Z-Alizadeh Sani data set that consists of 56 features including the class attribute collected from 303 patients. 16 of the 56 features are selected by ABC based updated feature selection method. Also, accuracy and F-measure values are measured as 89.4% and 0.894 respectively, which are higher than the values produced by the raw dataset.eninfo:eu-repo/semantics/closedAccessArtificial Bee Colony AlgorithmClassificationData MiningFeature SelectionZ-Alizadeh Sani DatasetConference Object6260WOS:000455592800007N/A