Do Neural Networks Preserve Case Structure? Case-Based Decomposition, Interpretation, and Decision Consistency
Read the original on arXiv AI →The paper investigates whether neural networks retain a case-based structure in their learned representations, enabling the decomposition of decision margins into contributions from individual training cases. By linking neural networks to Case-Based Decision Theory (CBDT), the authors identify conditions under which this recovered structure can be interpreted within CBDT and demonstrate that the resulting interpretation is consistent with the network’s original decisions. Experiments on a controlled CBDT setting and three real-world decision tasks confirm the viability of this approach.
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