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A new algorithm for incremental Bayesian classifier is proposed in this paper. The algorithm has two advantages. First, because our algorithm is based on the score function, it can make full use of the prior knowledge. Meanwhile, it can take the degree of fit between the added instances and the original classifier into consideration. Second, our algorithm expand incremental model from the naïve Bayesian...
To make the structure of attribute variables in Naiumlve Bayesian classifier (NB) or Tree Augmented Naive Bayesian classifier (TAN) more flexible and improve the accuracy of classification, a new Bayesian classifier called SubTree Augmented Naive Bayesian classifier (STAN) is proposed in this paper. It adopts the fuzzy equivalence partition approach to partition attribute variables into several subsets...
Data mining techniques, especially classification methods, are receiving increasing attention from researchers and practitioners in the domain of petroleum exploration and production (E&P) in China. To extensively investigate the effects of feature selection and learning algorithms on the hydrocarbon reservoir prediction performance, taking three real-world multiclass problems as examples, namely...
In this paper, a distributed customer classification model based on improved Bayesian network was proposed to solve a distributed customer classification problem. First, using mobile agents which could visit distributed data-sets, the multi-attributes tree and the Bayesian network were built. Then, all the distributed data-sets were trained by Bayesian network structure learning and parameter learning...
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