C4.5: programs for machine learning
C4.5: programs for machine learning
Bayesian Approach to Image Interpretation
Bayesian Approach to Image Interpretation
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
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Bayesian averaging over Decision Trees (DTs) allows the class posterior probabilities to be estimated, while the DT models are understandable for domain experts. The use of Markov Chain Monte Carlo (MCMC) technique of stochastic approximation makes the Bayesian DT averaging feasible. In this paper we describe a new Bayesian MCMC technique exploiting a sweeping strategy allowing the posterior distribution to be estimated accurately under a lack of prior information. In our experiments with the solar flares data, this technique has revealed a better performance than that obtained with the standard Bayesian DT technique.