Abstract
Naive Bayes (NB) classifier is a simple and efficient classifier, but the independent assumption of its attribute limits the application of the actual data. This paper presents an approach called Differential Evolution-Naive Bayes (DE-NB) which takes advantage of combining differential evolution with naive Bayes for attribute selection to improve naive Bayes classifier. This method applies DE firstly to search out an optimal subset of attributes reduction in the original attribute space, and then constructs a naive Bayes classifier on the gotten subset of the attributes reduction. Nineteen experimental results on UCI datasets distinctly show that compared with Cfs-BestFirst algorithm, NB algorithm, Support Vector Machine (SVM) algorithm, Decision Tree (C4.5) algorithm, K-neighbor (KNN) algorithm, the proposed algorithm has higher classification accuracy.
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Acknowledgements
This research is supported by National Natural Science Foundation of China under Grant No. 61273303. The authors thank professor Jiang Liangxiao for his very useful comments and suggestions.
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Li, J., Fang, G., Li, B., Wang, C. (2015). A Novel Naive Bayes Classifier Model Based on Differential Evolution. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_55
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DOI: https://doi.org/10.1007/978-3-319-22180-9_55
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