Feature Discovery for Inductive Concept Learning

This paper describes Zenith, a discovery system that performs constructive induction. The system is able to generate and extend new features for concept learning using agenda-based heuris- tic search. The search is guided by feature worth (a composite measure of discriminability and cost). Zenith is distinguished from existing constructive induction systems by its interaction with a performance system, and its ability to extend its knowledge base by creating new domain classes. Zenith is able to discover known useful features for the Othello board game.

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