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| Discriminative Learning of TAN Classifier Based on KL Divergence |
| FENG Qi, TIAN Feng-Zhan, HUANG Hou-Kuan |
| School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044 |
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Abstract Tree-augmented Nave bayes (TAN) classifier is a compromise between model complexity and classification rate. It is a hot research topic currently. To improve the classification accuracy of TAN classifier, a discriminative method based on Kullback-Leibler (KL) divergence is proposed. Explaining away residual (EAR) method is used to learn the structure of TAN, and then the TAN parameters are obtained by an objective function based on KL divergence. The experimental results on benchmark datasets show that the proposed method can get relatively high classification rates.
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Received: 01 June 2007
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2008, Vol. 21 
