Empirical Studies and Analysis of Ensemble Learning Techniques in Data Mining

Authors(2) :-Bhavesh Patankar, Dr. Vijay Chavda

This Classification using ensemble generally combines multiple classifiers that results in the improvement in the accuracy of the classification. Experimenting with the same dataset using the single classifier provides lesser accuracy than ensemble techniques. Many researches have been carried out using the technique of combining the predictions of multiple classifiers to generate a single classifier. The produced classifiers provide more accurate results than any individual classifier. This paper focuses on the ability of ensemble techniques to improve the accuracy of basic J48 algorithm. Ensemble techniques like Bagging and Boosting improved the efficiency of the J48 classifier. Experiments have been carried out on many datasets taken from UCI repository to investigate the effects of ensemble techniques on J48 and Naīve Bayes algorithm. WEKA tool is used to measure the effectiveness of a classifier model.

Authors and Affiliations

Bhavesh Patankar
Research Scholar, Department of Computer Science, Hemchandracharya North Gujarat University, Patan, Gujarat, India
Dr. Vijay Chavda
NPCCSM, Kadi Sarva VishwaVidyalaya, Gandhinagar, Gujarat, India

Ensemble, Boosting, Bagging, J48, Naīve Bayes

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Publication Details

Published in : Volume 2 | Issue 5 | September-October 2016
Date of Publication : 2015-10-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 80-82
Manuscript Number : IJSRSET162524
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Bhavesh Patankar, Dr. Vijay Chavda, " Empirical Studies and Analysis of Ensemble Learning Techniques in Data Mining , International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 5, pp.80-82, September-October.2016
URL : http://ijsrset.com/IJSRSET162524

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