Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Uncensoring censored data for machine learning: A likelihood-based approach
Expert Systems with Applications: An International Journal
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Learning directed probabilistic networks from data and using them for classification purposes is a well known problem. Many learning algorithms have been shown to be successful for various kinds of learning scenarios. Basically they all generate a single network from data, which is then used for classification purposes and possible domain understanding. In this paper we propose a simple method for inferring a model consisting of several Bayesian networks, each one representing data of one class. The data is divided into class subsets and from each subset a separate Bayesian network is learnt. Classification is done using prior and posterior probability distribution information in all networks. We thoroughly tested the proposed method on synthetic data and several repository datasets and compared it to other machine learning methods, to prove its effectiveness. We argue that with smaller modifications, the method can be used for learning from censored survival domains.