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This paper proposes a rule-plus-exemplar classification system to deal with the concept growth problem. Unlike concept drift, the concept is expanding with time rather than becoming obsolete. The proposed system is able to grow and evolve to incrementally learn the concept. It also adapts to the change to provide reliable classification even when the sample is unfamiliar with respect to the available...
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...
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