A New Ensemble Algorithm Based on Oppositional Relabeling of Artificial Data

The amount of data increases rapidly, and the types of data need to be handled become more and more various, a new algorithm with better generalization performance and higher classification accuracy is indispensable. In this paper, a new hybrid algorithm is proposed, which takes the advantage of the insensitivity to the input data of the Diversity Ensemble Creation by Oppositional Relabeling of Artificial Training Examples (DECORATE) algorithm and the efficiency of the radial basis functions neural network model. Asymptotic P value to decide the relationship between the area under receiver operator characteristic with 0.5 which belong to redundant features, and the oppositional relabeling artificial data is used to train the classifier. Then the new classifier is added which will lower training error get down to the original model, and the most vote is used to get the decision fusion result. Finally, this method is applied to UCI dataset, the results show that it can adapt to the different kinds of data and give the higher accuracy of classification.