One Dependence Augmented Naive Bayes

In real-world data mining applications, an accurate ranking is same important to a accurate classification. Naive Bayes (simply NB) has been widely used in data mining as a simple and effective classification and ranking algorithm. Since its conditional independence assumption is rarely true, numerous algorithms have been proposed to improve Naive Bayes, for example, SBC[1] and TAN[2]. Indeed, the experimental results show that SBC and TAN achieve a significant improvement in term of classification accuracy. However, unfortunately, our experiments also show that SBC and TAN perform even worse than naive Bayes in ranking measured by AUC[3, 4](the area under the Receiver Operating Characteristics curve). This fact raises the question of whether can we improve Naive Bayes with both accurate classification and ranking? In this paper, responding to this question, we present a new learning algorithm called One Dependence Augmented Naive Bayes (simply ODANB). Our motivation is to develo...

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