Artificial Bee Colony Algorithm for Feature Selection on SCADI Dataset
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Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Data 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 Karaboga in 2005, is implemented as feature selection algorithm (ABC-FS) and used on Self-Care Activities Dataset based on ICF-CY (SCADI). 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 % 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. © 2018 IEEE.
Açıklama
3rd International Conference on Computer Science and Engineering, UBMK 2018 -- 20 September 2018 through 23 September 2018 -- Sarajevo -- 150987
Anahtar Kelimeler
Artificial Bee Colony algorithm, Classification, Data mining, Feature selection, SCADI dataset
Kaynak
UBMK 2018 - 3rd International Conference on Computer Science and Engineering
