Comparative Analysis on Ensemble Learning: Bagging and Boosting
Date
2020
Authors
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Volume Title
Publisher
Department of Computer Science and Information Technology
Abstract
Combine the prediction from multiple models to improve the overall performance of
model is an ultimate task of Ensemble learning. Bagging and Boosting are two widely
used ensemble learning techniques works based on numbers of classifiers
combination to aggregate prediction. Performance of single classifier has limitation
due to noise, bias and variance in data set. By applying divide and conquer approach
on ensemble methods helps to minimize those limitation which ultimately leads to
performance improvement. Bagging is a bootstrap aggregation while boosting
attempts to fit a sequence of weak learner's models to build a strong classifier. The
performance of bagging and boosting has been analyzed on the basis of Accuracy,
Precision, Recall and F1-Measures for Adult data set with and without noise. The
Gaussian noise distribution has used for noise addition on data set due to CLT. The
results show that on the basis of Accuracy, Recall, F1-Measures boosting outperforms
bagging whereas in terms of Precision, bagging has better result.
Keywords: Ensemble Learning, Bagging, Boosting, Gaussian Noise
Description
Keywords
Comparative Analysis, Ensemble Learning, Bagging, Boosting