Learning Selective Averaged One-Dependence Estimators for Probability Estimation

Naïve Bayes is a well-known effective and efficient classification algorithm, but its probability estimation performance is poor. Averaged One-Dependence Estimators, simply AODE, is a recently proposed semi-naïve Bayes algorithm and demonstrates significantly high classification accuracy at a modest cost. In many data mining applications, however, accurate probability estimation is more desirable when making optimal decisions. Usually, probability estimation performance is measured by conditional log likelihood (CLL). In this paper, we first study the probability estimation performance of AODE and compare it to naïve Bayes, TreeAugumented naïve Bayes, CLLTree, C4.4 (the improved version of C4.5 for better probability estimation) and Support Vector Machines. From our experiments, we find that AODE performs significantly better than the algorithms used to compare except C4.4, and performs slightly better than C4.4 although its classification accuracy is significantly better than C4....

• Type: Conference
• Year: 2007
• Where: FSKD
• Authors: Qing Wang, Chuan-hua Zhou, Jiankui Guo
• Keywords: FSKD 2007, Naive Bayes, Probability Estimation, Probability Estimation Performance