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    Feature Selection with Artificial Bee Colony Algorithm on Z-Alizadeh Sani Dataset
    (Institute of Electrical and Electronics Engineers Inc., 2018) Kiliç, Ümit; Kayakeleş, Mümine
    Relevant 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. © 2018 IEEE.
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    Öğe
    Prediction of concrete strength with data mining methods using artificial bee colony as feature selector
    (Institute of Electrical and Electronics Engineers Inc., 2019) Kaya Keleş, Mümine; Keleş, Abdullah Emre; Kiliç, Ümit
    Concrete which is a highly complex material is the most basic input of the construction industry. Because of its strength, concrete is one of the most preferred structural building materials. In the ready-mixed concrete sector, there is an increasing need for earthquake resistant structures due to the fact that some producers produce out of control and poor quality. Ready-mixed concrete is a product whose quality can only be understood at the end of the 28th day if it is only controlled by taking the sample by the user. In this study, a data mining study was conducted on the factors affecting the 28-day compressive strength of concrete using the Concrete Slump Test Data Set from UCI Machine Learning Repository. The Artificial Bee Colony Algorithm is used as a feature selection method in order to determine the important ones of the concrete components, which are cement, slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate, affecting concrete strength and tried to predict the strength with data mining algorithms. As a result of the study, it was observed that Random Forest Algorithm gave the highest success rate with 91.2621% accuracy using only 3 features, which are cement, fly ash, and water. This means that it is possible to predict the compressive strength of concrete with a ratio above 90% by using a smaller number of concrete components. © 2018 IEEE.