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This paper presents an approach to the problem of binary classification using ensemble neural networks based on interval neutrosophic sets and bagging technique. Each component in the ensemble consists of a pair of neural networks trained to predict the degree of truth and false membership values. Uncertainties in the prediction are also estimated and represented using the indeterminacy membership values. These three membership values collectively form an interval neutrosophic set. In order to combine and classify outputs from components in the ensemble, the outputs of an ensemble are dynamically weighted and summed. The proposed approach has been tested with three benchmarking UCI data sets, which are ionosphere, pima, and liver. The proposed ensemble method improves the classification performance as compared to the simple majority vote and averaging methods which were applied only to the truth membership value. Furthermore, the results obtained from the proposed ensemble method also outperform the results obtained from a single pair of networks and the results obtained from a single truth network.