Classification Algorithm for Naïve Bayes Based on Validity and Correlation
Naïve Bayes classification algorithm based on validity (NBCABV) optimizes the training data by eliminating the noise samples of training data with validity to improve the effect of classification, while it ignores the associations of properties. In consideration of the associations of properties, an...
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| Veröffentlicht in: | Applied Mechanics and Materials 2013-02, Vol.303-306, p.1609-1612 |
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| container_title | Applied Mechanics and Materials |
| container_volume | 303-306 |
| creator | Zhu, Xiao Dan Huang, Juan Juan Dong, Huai Lin Wu, Qing Feng |
| description | Naïve Bayes classification algorithm based on validity (NBCABV) optimizes the training data by eliminating the noise samples of training data with validity to improve the effect of classification, while it ignores the associations of properties. In consideration of the associations of properties, an improved method that is classification algorithm for Naïve Bayes based on validity and correlation (CANBBVC) is proposed to delete more noise samples with validity and correlation, thus resulting in better classification performance. Experimental results show this model has higher classification accuracy comparing the one based on validity solely. |
| doi_str_mv | 10.4028/www.scientific.net/AMM.303-306.1609 |
| format | Article |
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| title | Classification Algorithm for Naïve Bayes Based on Validity and Correlation |
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