Building an Ensemble of Fine-Tuned Naive Bayesian Classifiers for Text Classification

Text classification is one domain in which the naive Bayesian (NB) learning algorithm performs remarkably well. However, making further improvement in performance using ensemble-building techniques proved to be a challenge because NB is a stable algorithm. This work shows that, while an ensemble of...

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Veröffentlicht in:	Entropy (Basel, Switzerland) Jg. 20; H. 11; S. 857
Hauptverfasser:	El Hindi, Khalil, AlSalman, Hussien, Qasem, Safwan, Al Ahmadi, Saad
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 07.11.2018 MDPI
Schlagworte:	Accuracy Algorithms Bayesian analysis Classification Classifiers Datasets ensembles of classifiers fine-tuning naive Bayesian algorithm Machine learning naive Bayesian learning Neural networks Probability distribution Text categorization text classification
ISSN:	1099-4300, 1099-4300
Online-Zugang:	Volltext
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