Boosted Bayesian Network Classifiers

Discriminative Graphical Models

In Collaboration with Dr. James M. Rehg and Yushi Jing, College of Computing, Georgia Institute of Technology.

Discriminative learning, or learning for classification, is a common learning task that has been addressed in a number of different frameworks.  One may design a complex classifier, such as a support vector machine, that explicitly minimizes classification error. Alternatively, a set of weak learners can be trained using the boosting algorithm [Schapire97].  However, one may be explicitly interested in constructing a generative model for classification, such as a Bayesian network. The option in that case is to discriminatively train this generative model.  Unfortunately, discriminative training of generative models is computationally complex [Friedman97,Greiner02,Grossman04].  On the other hand, if the model is trained in a generative ML fashion its strong reliance on correct structure and independence assumption often undermine its classification ability. 

In this project we study a new framework for discriminative training of generative models.  Similar to traditional boosting, we recursively learn a set classifiers, this time constructed from generative models. Unlike boosting, where weak classifiers are trained discriminatively, the ‘weak classifiers’ in our method are trained generatively, to maximize the likelihood of the weighted data.  This approach has two distinct benefits.  First, our classifiers are constructed from generative models.  This is important in many practical cases when generative models, such as Bayesian networks or HMM, are desired or appropriate (e.g., sequence modeling).  Second, the ML training of generative models is computationally more efficient than discriminative training of the same. Therefore, by discriminatively setting the weights on the data and by generatively training intermediate models we achieve a computationally efficient way of training generative classifiers.

Algorithms

Results


Publications

  1. Jing, Y., Pavlovic, V. & Rehg, J.M. (2008), “Boosted Bayesian network classifiers”, Machine Learning Journal.
  2. Jing, Y., Pavlovic, V. & Rehg, J.M. (2005) *Tech-report version (GIT-GVU-05-23)*  (Includes a preliminary analysis of boosted Dynamic Bayesian Network Classifiers, as an alternative to discriminative training methods like Conditional Random Fields )
  3. Jing, Y., Pavlovic, V. & Rehg, J.M. (2005) Efficient discriminative learning of Bayesian network classifiers via Boosted Augmented Naive Bayes – Proceedings of International Conference on Machine Learning (ICML 2005), Distinguished Student Paper Award.
  4. Jing, Y., Pavlovic, V. & Rehg, J.M. (2005) Discriminative Learning Using Boosted Generative Models – The Learning Workshop at Snowbird, Utah, April 5-8.


Software and Data

We have developed a C++ library for structure and parameter learning in boosted augmented Bayesian networks.  Please see our Software page for more details.

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