ENBFS+kNN: Hybrid ensemble classifier using entropy-based naïve Bayes with feature selection and k-nearest neighbor
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...
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| 主要な著者: | , , |
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| フォーマット: | Conference or Workshop Item |
| 出版事項: |
2016
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| 主題: | |
| オンライン･アクセス: | http://repo.uum.edu.my/19522/ http://doi.org/10.1063/1.4960933 |
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| 要約: | 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. |
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