Keles, Mumine KayaKılıç, Umit2025-01-062025-01-062018978-1-5386-7893-0https://hdl.handle.net/20.500.14669/18793rd International Conference on Computer Science and Engineering (UBMK) -- SEP 20-23, 2018 -- Sarajevo, BOSNIA & HERCEGData consists of relevant and irrelevant features. These irrelevant features may mislead classification or clustering algorithms. Feature selection algorithms are used to avoid that misleading and to obtain better results using fewer number of features than dataset has. The purpose of feature selection is to choose as small number of relevant features as possible to enhance the performance of the classification. In this paper, Artificial Bee Colony algorithm (ABC), which is proposed by karaboaa in 2005, is implemented as feature selection algorithm (ABC-FS) and used on Self-Care Activities Dataset based on ICF-CY (SCAM). SCADI that contains 206 attributes of 70 children with physical and motor disability is a dataset for self-care problems. Feature selection operation is carried out using Gain Ratio, Info Gain, and Chi Square. These selected features are utilized to obtain classification results. Then, features are selected by ABC-FS and results are compared. Seven of 206 features are selected by ABC-FS, and 88.5714% accuracy rate and 0.871 F-Measure value are obtained while best of Info Gain, Gain Ratio and Chi-Square is 84.2857 (1/0 and 0.824, respectively. The experimental results show that the selected features by ABC-FS generally have higher accuracy than the raw dataset and Info Gain, Gain Ratio and Chi-Square.eninfo:eu-repo/semantics/closedAccessArtificial Bee Colony algorithmClassificationData miningFeature selectionSCADI datasetConference Object466463WOS:000459847400089N/A