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Learning concept descriptions with typed evolutionary programming

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2 Author(s)
Thie, C.J. ; Malvern Technol. Centre, QinetiQ, Malvern, UK ; Giraud-Carrier, C.

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.

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Knowledge and Data Engineering, IEEE Transactions on  (Volume:17 ,  Issue: 12 )