Artificial Intelligence
Improving Naive Bayes Using Class-Conditional ICA
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Boosting Naive Bayes for Claim Fraud Diagnosis
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
A fundamental issue of naive bayes
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
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Naive Bayes is one of the most efficient and effective learning algorithms for machine learning, pattern recognition and data mining. But its conditional independence assumption is rarely true in real-world applications. We show that the independence assumption can be approximated by orthogonally rotational transformation of input space. During the transformation process, the continuous attributes are treated in different ways rather than simply applying discretization or assuming them to satisfy some standard probability distribution. Furthermore, the information from unlabeled instances can be naturally utilized to improve parameter estimation without considering the negative effect caused by missing class labels. The empirical results provide evidences to support our explanation.