A comparative study of classification techniques by utilizing WEKA

Abstract

In software engineering, information retrieval which is also referred as data mining has attracted many researcher's attention. By the virtue of its definition, data mining is responsible for extracting relevant data from large volume of database or dataset. In this context, several techniques have been proposed in literature. Through this paper, an attempt to comparative analysis of various classification algorithms has been made. Such analysis has been done with the help of data mining tool named WEKA on dataset of alcohol consumption by school students. WEKA is a open framework programming tool consisting of various inbuilt classification algorithms like J48, Random Forest, Decision Tree, Random Tree, NaiveBayes, SimpleNaiveBayes, NaiveBayes, DecisionStump, etc. However, the comparison of these algorithms has been made with the help of approaches that include correctly classified, incorrectly classified, Accuracy and many others parameters.