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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...
In pattern classification, the test data is expected to lie in the domain covered by the training data. But in practical scenarios, this may not necessarily be true. To improve the adaptability, the classifier should be able to generalize well even when there are changes in the input distribution. This paper proposes a cognitively inspired classification framework based on rules and exemplars. It...
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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