Anomaly Detection using Decision Tree based Classifiers
as we know that with the help of Data mining techniques we can find out knowledge in terms of various characteristics and patterns. In this regard this paper presents finding out of anomalies/ outliers using various decision tree based classifiers viz. Best-first Decision Tree, Functional Tree, Logistic Model Tree, J48 and Random Forest decision tree. Three real world datasets has been used in this study. Theoretical analysis and experimental results shown that the Random Forest decision tree has outperformed other decision tree based classifiers of this study in terms of correct classification rate and kappa statistic. Keywordsanomaly, outlier, decision tree, classification