A cognitively inspired rule-plus-exemplar framework for interpretable pattern classification
Wing Yee Sit, Kezhi Mao · DR-NTU (Nanyang Technological University) · 2012
While the generalizability of classifiers receive much attention in research, interpretability is often neglected. This paper proposes a rule-plus-exemplar classification framework based on ideas in cognitive psychology. The classification process is interpretable and intuitive, and also generalizes well. It can perform better than other interpretable methods such as decision trees, for both interpolative and extrapolative generalization.