Artificial Bee Colony Algorithm for Feature Selection on SCADI Dataset

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dc.authoridKilic, Umit/0000-0001-8067-6024
dc.contributor.authorKeles, Mumine Kaya
dc.contributor.authorKılıç, Umit
dc.date.accessioned2025-01-06T17:36:26Z
dc.date.available2025-01-06T17:36:26Z
dc.date.issued2018
dc.description3rd International Conference on Computer Science and Engineering (UBMK) -- SEP 20-23, 2018 -- Sarajevo, BOSNIA & HERCEG
dc.description.abstractData 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.
dc.description.sponsorshipBMBB,Istanbul Teknik Univ,Gazi Univ,ATILIM Univ,Int Univ Sarajevo,Kocaeli Univ,TURKiYE BiLiSiM VAKFI
dc.description.sponsorshipScientific Research Projects Commission Unit of Adana Science and Technology University [18332001, 18103004]
dc.description.sponsorshipThis study was supported by Scientific Research Projects Commission Unit of Adana Science and Technology University under Grant Number: 18332001 and Grant Number: 18103004.
dc.identifier.endpage466
dc.identifier.isbn978-1-5386-7893-0
dc.identifier.startpage463
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1879
dc.identifier.wosWOS:000459847400089
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2018 3rd International Conference on Computer Science and Engineering (Ubmk)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectArtificial Bee Colony algorithm
dc.subjectClassification
dc.subjectData mining
dc.subjectFeature selection
dc.subjectSCADI dataset
dc.titleArtificial Bee Colony Algorithm for Feature Selection on SCADI Dataset
dc.typeConference Object

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