Towards a Transparent Deep Ensemble Method Based on Multiagent Argumentation

Ensemble methods improve the machine learning results by combining different models. However, one of the major drawbacks of these approaches is their opacity, as they do not provide results explanation and they do not allow prior knowledge integration. As the use of machine learning increases in critical areas, 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 new deep ensemble method based on argumentation that combines machine learning algorithms with a multiagent system in order to explain the results of classification and to allow injecting prior knowledge. The idea is to extract arguments from classifiers and combine the classifiers using argumentation. This allows to exploit the internal knowledge of each classifier, to provide an explanation for the decisions and facilitate integration of domain knowledge. The results demonstrate that our method effectively improves deep learning performance in addition to providing explanations and transparency of the predictions.