Learning Tree Augmented Naive Bayes for Ranking

Naive Bayes has been widely used in data mining as a simple and effective classification algorithm. Since its conditional independence assumption is rarely true, numerous algorithms have been proposed to improve naive Bayes, among which tree augmented naive Bayes (TAN) [3] achieves a significant improvement in term of classification accuracy, while maintaining efficiency and model simplicity. In many real-world data mining applications, however, an accurate ranking is more desirable than a classification. Thus it is interesting whether TAN also achieves significant improvement in term of ranking, measured by AUC(the area under the Receiver Operating Characteristics curve) [8, 1]. Unfortunately, our experiments show that TAN performs even worse than naive Bayes in ranking. Responding to this fact, we present a novel learning algorithm, called forest augmented naive Bayes (FAN), by modifying the traditional TAN learning algorithm. We experimentally test our algorithm on all the 36 ...

• Type: Conference
• Year: 2005
• Where: DASFAA
• Authors: Liangxiao Jiang, Harry Zhang, Zhihua Cai, Jiang Su
• Keywords: Accurate Ranking, DASFAA 2005, Data Mining, Database, Naive Bayes