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ENBFS+kNN: Hybrid ensemble classifier using entropy-based naïve Bayes with feature selection and k-nearest neighbor


Sainin, Mohd Shamrie and Alfred, Rayner and Ahmad, Faudziah (2016) ENBFS+kNN: Hybrid ensemble classifier using entropy-based naïve Bayes with feature selection and k-nearest neighbor. In: 2016 International Conference on Applied Science and Technology (ICAST 2016), 11–13 April 2016, Kedah, Malaysia.

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Abstract

A hybrid ensemble classifier which combines the entropy based naive Bayes (ENB) classifier strategy and k-nearest neighbor (k-NN) is examined.The classifiers are joined in light of the fact that naive Bayes gives prior estimations taking into account entropy while k-NN gives neighborhood estimate to model for a deferred characterization. While original NB utilizes the probabilities, this study utilizes the entropy as priors for class estimations. The result of the hybrid ensemble classifier demonstrates that by consolidating the classifiers, the proposed technique accomplishes promising execution on several benchmark datasets.

Item Type: Conference or Workshop Item (Paper)
Additional Information: ISBN: 978-0-7354-1419-8
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Computing
Depositing User: Mr. Mohd. Shamrie Sainin
Date Deposited: 15 Nov 2016 04:02
Last Modified: 15 Nov 2016 04:02
URI: http://repo.uum.edu.my/id/eprint/19522

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