One-Pass Learning Algorithm for Fast Recovery of Bayesian Network

An efficient framework is proposed for the fast recovery of Bayesian network classifier. A novel algorithm, called Iterative Parent-Child learningBayesian Network Classifier (IPC-BNC), is proposed to learn a BNC without having to learn the complete Bayesian network first. IPC-BNC was proved correct and more efficient compared with a traditional global learning algorithm, called PC, by requiring much fewer conditional independence (CI) tests. Besides, we recognize and introduce AD-tree into the implementation so that computational efficiency is further increased through collecting full statistics within a single data pass. The IPC-BNC and AD-tree combination is demonstrated very efficient in time by our empirical study, making itself an attractive solution in very large applications.

• Type: Conference
• Year: 2008
• Where: FLAIRS
• Authors: Shunkai Fu, Michel Desmarais, Fan Li
• Keywords: Artificial Intelligence, Bayesian Network, Bayesian Network Classifier, FLAIRS 2008, Network Classifier