Incremental Concept Evolution Based on Adaptive Feature Weighting

We provide a model of concept drift by which concept classes are automatically updated according to the dynamics of change in the underlying learning environment. Instances with a well-defined degree of feature similarity are incrementally grouped together to form so-called concept versions. A comparative empirical evaluation of the versioning system VerGene, which is built on these premises, demonstrates its effectiveness for properly dealing with concept drift phenomena in medium-sized knowledge bases.

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