A new Transparent Ensemble Method based on Deep learning

Rather than making one model and hoping this model is the best/most accurate predictor we can make, ensemble methods which improve machine learning results by combining different models. However, one of the major criticisms is their being inexplicable, since they do not provide results explanation and do not allow prior knowledge integration. With the development of the machine learning the explanation of classification results and the ability to introduce domain knowledge inside the learned model have become a necessity. In this paper, we present a novel deep ensemble method based on argumentation that combines machine learning algorithms with multi-agent system to improve classification. The idea is to extract arguments from classifiers and to combine them using argumentation in order to exploit the internal knowledge of each classifiers and provide explanation behind decisions and to allow injecting prior knowledge. The results demonstrate that our method can effectively extract high quality knowledge for ensemble classifier and improve the performance.