A copula-based visualization technique for a neural network

• URL: http://arxiv.org/abs/2003.12317v1
• Date: Fri, 27 Mar 2020 10:32:27 GMT
• Title: A copula-based visualization technique for a neural network
• Authors: Yusuke Kubo, Yuto Komori, Toyonobu Okuyama, Hiroshi Tokieda
• Abstract summary: Interpretability of machine learning is defined as the extent to which humans can comprehend the reason of a decision. We propose a new algorithm that reveals which feature values the trained neural network considers important.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interpretability of machine learning is defined as the extent to which humans can comprehend the reason of a decision. However, a neural network is not considered interpretable due to the ambiguity in its decision-making process. Therefore, in this study, we propose a new algorithm that reveals which feature values the trained neural network considers important and which paths are mainly traced in the process of decision-making. In the proposed algorithm, the score estimated by the correlation coefficients between the neural network layers that can be calculated by applying the concept of a pair copula was defined. We compared the estimated score with the feature importance values of Random Forest, which is sometimes regarded as a highly interpretable algorithm, in the experiment and confirmed that the results were consistent with each other. This algorithm suggests an approach for compressing a neural network and its parameter tuning because the algorithm identifies the paths that contribute to the classification or prediction results.

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