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An intelligent learning model for stochastic data

In the real world, uncertainty in the data is a frequently confronted difficulty problem for learning system. The performance of the learning method can be deteriorated by the uncertainty. To properly represent and handle the uncertainty problem becomes one of the key issues in the decision learning field. An intelligent learning model is presented in this paper to address the uncertainty problem. The noise-insensitive feature of the Naïve Bayesian classifier is used to enhance the noise-tolerant ability of probabilistic information based Support Vector Machine. The intelligent learning model conducts a flexible strategy to integrate the two models, based on the probabilistic decision information obtained from the two classifiers. Then, it gives the final decision. Furthermore, the intelligent learning model is evaluated on an artificial dataset for a classification task. The experiment results show good performance when compared with using only one technique in the noise environment.