Abstract
The goal of this study is to empirically examine classification performances of multilayer perceptron (MLP), logistic regression (LR), adaptive neuro-fuzzy inference system (ANFIS), bagging classification and regression trees (CART) and k-nearest neighbor (KNN) methods. For 1000-times repetitive simulation, four correlated (0≤r≤0.95) independent variables with normal distribution and dichotomy dependent variable were created. Sample size was set to n=250, 500, 750 and 1000. Besides, Wisconsin breast cancer (WBC) data set was also used to compare performances of the models. All models except ANFIS showed better performance as sample size increased. Descending order of model accuracy rates was found as ANFIS, MLP, LR, KNN, bagging CART both in simulation and WBC data. Besides, comparison results were similar according to sensitivity and specificity to accuracy rates.
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