A rule-plus-exemplar classification system for adapting to concept growth

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 training data. A series of experimental results with comparable methods show that the system can perform better under concept growth circumstances.