标题：Interpretable Neural Network Construction: From Neural Network to Interpretable Neural Tree (Open Access)
作者：Ouyang, Xuming ;Feng, Cunguang
作者机构：[Ouyang, Xuming ] School of Software, Shandong University, Jinan, Shandong; 250101, China;[Feng, Cunguang ] School of Information Science and Technolo 更多
会议名称：2020 4th International Workshop on Advanced Algorithms and Control Engineering, IWAACE 2020
会议日期：February 21, 2020 - February 23, 2020
来源：Journal of Physics: Conference Series
摘要：The neural network has made outstanding achievements in many fields, while comparing with traditional machine learning models, the neural network has poor interpretability, which brings great limitation to its practical application. Therefore, many researchers try to combine neural networks with traditional models to improve the interpretability of the neural network. Their methods either result in performance depreciation or lead to computation-intensive. In this paper, we propose to transform a neural network into the interpretable neural tree. In the interpretable neural tree, each node contains transformation function and routing function. Each transformation function corresponds to a layer in the neural network, using for controlling data transformation. The routing function is utilized to control the direction of data flow in the tree structure. Our experiments have indicated that the interpretable neural tree makes the neural network interpretable to some extent while maintaining the performance.
© Published under licence by IOP Publishing Ltd.