一种用于发现特征相关性并解释神经分类器决策的集合隶属方法
A Set Membership Approach to Discovering Feature Relevance and Explaining Neural Classifier Decisions
AI总结：
本文提出一种基于区间分析的集合隶属非线性参数估计方法，用于识别训练后神经分类器所依赖的相关特征及其对输出的影响，从而为黑箱决策提供数学上可靠的解释。
摘要：
神经分类器是非线性系统，针对其已学习的给定问题提供模式类别的决策。分类器为每个模式计算的输出构成了某个未知函数输出的近似，该函数将模式数据映射到它们各自的类别。由于对此类函数缺乏了解，加上神经分类器的复杂性，尤其是当这些分类器属于深度学习架构时，人们无法获得关于具体预测是如何做出的信息。因此，这些强大的学习系统被视为黑箱，在关键应用中，它们的使用往往被认为并不合适。深入了解这种黑箱运行方式，构成了解释神经分类器运行并评估其决策有效性的一条单向途径。本文中，我们通过引入一种新颖方法来解决这一问题，该方法用于发现经过训练的神经分类器认为哪些特征具有相关性，以及这些特征如何影响分类器的输出，从而获得对其决策的解释。尽管特征相关性已在机器学习文献中受到广泛关注，但我们在此依据由集合隶属方法所针对的非线性参数估计来重新审视它，而该集合隶属方法以区间分析为基础。因此，所提出的方法建立在可靠的数学方法之上，所得结果构成了对分类器决策前提的可靠估计。
英文摘要：
Neural classifiers are non linear systems providing decisions on the classes of patterns, for a given problem they have learned. The output computed by a classifier for each pattern constitutes an approximation of the output of some unknown function, mapping pattern data to their respective classes. The lack of knowledge of such a function along with the complexity of neural classifiers, especially when these are deep learning architectures, do not permit to obtain information on how specific predictions have been made. Hence, these powerful learning systems are considered as black boxes and in critical applications their use tends to be considered inappropriate. Gaining insight on such a black box operation constitutes a one way approach in interpreting operation of neural classifiers and assessing the validity of their decisions. In this paper we tackle this problem introducing a novel methodology for discovering which features are considered relevant by a trained neural classifier and how they affect the classifier's output, thus obtaining an explanation on its decision. Although, feature relevance has received much attention in the machine learning literature here we reconsider it in terms of nonlinear parameter estimation targeted by a set membership approach which is based on interval analysis. Hence, the proposed methodology builds on sound mathematical approaches and the results obtained constitute a reliable estimation of the classifier's decision premises.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
