A Combined Classification A lgorithm Based on C4.5 and N B
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We present C5.0 and NB are two very essential algorithms for achieving this task because of their ease and high performance. In this paper, we present a hybrid classification algorithm founded on C5.0 and NB, simply C5.0-NB. In C5.0-NB, the class chance estimates of C5.0 and NB are weighted according to their classification accuracy on the training data. The experimental results show that C5.0-NB significantly outperforms C5.0 and NB in terms of classification accuracy. We have collect dataset from UCI A Bayesian network consists of a structural model and a set of conditional probabilities. The structural model is a directed acyclic graph in which nodes represent attributes and arcs represent attribute dependencies. Attribute dependencies are quantified by conditional probabilities for each node given its parents. The Bayesian network is often used for classification problems, in which a learner attempts to construct a Bayesian network classifier from a given set of training instances with class labels. Assume that all attributes are independent given the class, then the resulting Bayesian network classifier is called the naive Bayesian classifier, simply NB
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