Comparison of Bagging and Voting Ensemble Machine Learning Algorithm as a Classifier

Ledisi Giok Kabari

Abstract


Bagging and Voting are both types of ensemble learning, which is a type of machine learning where multiple classifiers are combined to get better classification results. This paper presents an experimental comparison of Bagging and Voting ensemble machine learning algorithms. The iris dataset which has 150 data instances and 5 attributes was used to conduct the experiment.  It was observed that bagging is a better ensemble learning algorithm than voting based on the experimental data used for classification.

 

Key words: Bagging, Voting, Machine Learning, Classifier, Algorithm, dataset.


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References


REFERENCES

Analytics Vidhya (July, 2018) Basics of Ensemble learning explained in simple term, Retrieved from https://www.analyticsvidhya.com/blog/2015/08/introduction-ensemble-learning.

Samuel, Arthur (1959), "Some Studies in Machine Learning Using the Game of Checkers". IBM Journal of Research and Development, 3 (3): 210–229.

Tom M. Mitchell (1997), Machine Learning, McGraw Hill, P. 2. ISBN 0-07-042807-7.

Kaggle (October, 2018) Iris dataset, start learning Machine Learning, available online at

https:/ /www.kaggle.com/ashishs0ni/iris-dataset




DOI: https://doi.org/10.23956/ijarcsse.v9i3.948

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