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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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Vol. 20; no. 11; p. 857
Main Authors: El Hindi, Khalil, AlSalman, Hussien, Qasem, Safwan, Al Ahmadi, Saad
Format: Journal Article
Language:English
Published: Basel MDPI AG 07.11.2018
MDPI
Subjects:
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 Access:Get full text
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Summary: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 NB classifiers achieves little or no improvement in terms of classification accuracy, an ensemble of fine-tuned NB classifiers can achieve a remarkable improvement in accuracy. We propose a fine-tuning algorithm for text classification that is both more accurate and less stable than the NB algorithm and the fine-tuning NB (FTNB) algorithm. This improvement makes it more suitable than the FTNB algorithm for building ensembles of classifiers using bagging. Our empirical experiments, using 16-benchmark text-classification data sets, show significant improvement for most data sets.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e20110857