Assigning Weights to Training Instances Increases Classification Accuracy

The decision tree (DT) approach is most useful in classification problem. In conventional decision tree learning the weights of every training instances are set to one or equal value, which contradicts general intuition. In this paper, we proposed a new decision tree learning algorithm by assigning appropriate weights to each training instance in the training data that increases classification accuracy of the decision tree model. The main advantage of this proposed approach is to set appropriate weights to training instances using na ve Bayesian classifier before trying to construct the decision tree. In our approach the training instances are assigned to weight values based on the posterior probability. The training instances having less weight values are either noisy or posses unique characteristics compared to other traininginstances. The experimental results manifest that the proposed approach for decision tree construction can achieve high classification accuracy with compare to traditional decision tree algorithms on different types of benchmark datasets from UCI machine learning repository.