MBNR: Case-Based Reasoning with Local Feature Weighting by Neural Network

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Hybrid case-based reasoning system by cost-sensitive neural network for classification (2016, methodology): “Therefore, many network pruning tasks are attempted and implemented (Shin and Park 1999; Shin et al. 2000; Im and Park 2007; Park et al. 2004; Ha 2008; Zeng and Martinez 2004; Yang and Jin 2010; Peng and Zhuang 2007; Sarwar et al. 2010; Park et al. 2006) to find feature weights.”
How Case-Based Reasoning Explains Neural Networks: A Theoretical Analysis of XAI Using Post-Hoc Explanation-by-Example from a Survey of ANN-CBR Twin-Systems (2019, methodology): “Shin and colleagues [46, 47, 51] appear to be the first in the literature, to explicitly pair ANNs and CBR systems in a twinned way for purposes of explanation and to perform comparative tests of different feature-weighting schemes.”