Comparison of Classification Methods Based on the Type of Attributes and Sample Size

In this paper, the efficacy of seven data classification methods; Decision Tree (DT), k-Nearest Neighbor (k-NN), Logistic Regression (LogR), Naïve Bayes (NB), C4.5, Support Vector Machine (SVM) and Linear Classifier (LC) with regard to the Area Under Curve (AUC) metric have been compared. The effects of parameters including size of the dataset, kind of the independent attributes, and the number of the discrete and continuous attributes have been investigated. Based on the results, it can be concluded that in the datasets with few numbers of records, the AUC become deviated and the comparison between classifiers may not do correctly. When the number of the records and the number of the attributes in each record are increased, the results become more stable. Four classifiers DT, k-NN, SVM and C4.5 obtain higher AUC than three classifiers LogR, NB and LC. Among these four classifiers, C4.5 provides higher AUC in the most cases. As a comparison among three classifiers LogR, NB and LC, it can be said that NB provides the best AUC among them and classifiers LogR and NB have the same results, approximately.