Koc,E.Ozer,A.N.2024-10-152024-10-1520150978-989853339-5[SCOPUS-DOI-BELIRLENECEK-86]2-s2.0-84970021266https://hdl.handle.net/20.500.14517/6802Feature selection, also known as attribute selection, is a process which attempts to select more informative features among datasets to be used in model construction. The main aim of feature selection can improve the prediction accuracy and reduce the computational overhead of classification algorithms. In this study, several approaches such as Information Gain Attribute Evaluation, Chi-Squared Attribute Evaluation, Filtered Attribute Evaluation, Gain Ratio Attribute Evaluation and Symmetrical Uncertainty Attribute Evaluation are carried out to discover the discriminative features on the same disease, namely lung cancer, using four different medical datasets. The efficiency of each approach is evaluated using machine learning software.eninfo:eu-repo/semantics/closedAccessChi-squared attribute evaluationFeature selectionFiltered attribute evaluationGain ratio attribute evaluationInformation gain attribute evaluationSymmetrical uncertainty attribute evaluationConference Object219222