Learning class-discriminative dynamic Bayesian networks

In many domains, a Bayesian network's topological structure is not known a priori and must be inferred from data. This requires a scoring function to measure how well a proposed network topology describes a set of data. Many commonly used scores such as BD, BDE, BDEU, etc., are not well suited for class discrimination. Instead, scores such as the classconditional likelihood (CCL) should be employed. Unfortunately, CCL does not decompose and its application to large domains is not feasible. We introduce a decomposable score, approximate conditional likelihood (ACL) that is capable of identifying class discriminative structures. We show that dynamic Bayesian networks (DBNs) trained with ACL have classification efficacies competitive to those trained with CCL on a set of simulated data experiments. We also show that ACL-trained DBNs outperform BDE-trained DBNs, Gaussian na?ve Bayes networks and support vector machines within a neuroscience domain too large for CCL.

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: John Burge, Terran Lane
• Keywords: Approximate Conditional Likelihood, Class Discriminative Structures, Dynamic Bayesian Networks, ICML 2005, Machine Learning