Paper Title
A Performance Comparison of End, Bagging and Dagging Meta Classification Algorithms

Abstract
Data mining is a technology that blends traditional data analysis methods with sophisticated algorithms for processing large volume of data. Classification is an important data mining technique with broad applications. Classification is a supervised procedure that learns to classify new instances based on the knowledge learnt from a previously classified training set of instances. It is used to classify each item in a set of data into one of predefined set of classes or groups. There are various Meta classification algorithms such as Decorate, Attribute Selected Classifier, Bagging, Dagging, Filtered Classifier, LogitBoost, END, Dagging, Rotation Forest, and so on. In this research work, we have analyzed the performance of three Meta classification algorithms namely END, bagging and dagging. For comparing the three algorithms, the performance parameters namely classification accuracy and error rate was used. A dataset in the form of arff file was downloaded from UCI machine learning repository. This dataset contains 20000 instances and seventeen attributes. The simulations were done by using WEKA open source tool. From the experimental results, it is analyzed that END algorithm performs better than other algorithms. Keywords� Data Mining, Meta Classification, Bagging, Dagging, END, WEKA.