A comparative analysis of various classification trees

Data mining deals with large voluminous data. Classification of data is an important task in data mining process that extracts models for describing classes and predicts target class for data instances. Today several standard classifiers are available. In this paper, divergent datasets from University of California, Irvine(UCI) are classified using Weka Explorer with different classification trees like Decision Stump, Hoeffding tree, J48, LMT, Random forest and REP tree. Then classification is also done by importing Weka Library for classification trees to NetbeansIDE. Finally results of both methodologies are compared using the performance measures like Accuracy and execution time.