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ABSTRACT
Examples and concepts in traditional concept learning tasks are represented with the attribute-value language. While enabling efficient implementations, we argue that such propositional representation is inadequate when data is rich in structure. This paper describes STEPS, a strongly-typed evolutionary programming system designed to induce concepts from structured data. STEPS' higher-order logic representation language enhances expressiveness, while the use of evolutionary computation dampens the effects of the corresponding explosion of the search space. Results on the PTE2 challenge, a major real-world knowledge discovery application from the molecular biology domain, demonstrate promise.
INDEX TERMS
Index Terms- Concept learning, typed evolutionary programming.
CITATION
Claire J. Thie, Christophe Giraud-Carrier, "Learning Concept Descriptions with Typed Evolutionary Programming", IEEE Transactions on Knowledge & Data Engineering, vol. 17, no. , pp. 1664-1677, December 2005, doi:10.1109/TKDE.2005.199
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