%0 Journal Article %1 10.1109/TKDE.2005.199 %A Thie, Claire J. %A Giraud-Carrier, Christophe %C Los Alamitos, CA, USA %D 2005 %I IEEE Computer Society %J IEEE Transactions on Knowledge and Data Engineering %K Concept STGP, algorithms, evolutionary genetic learning, programming programming, typed %N 12 %P 1664--1677 %T Learning Concept Descriptions with Typed Evolutionary Programming %U http://doi.ieeecomputersociety.org/10.1109/TKDE.2005.199 %V 17 %X 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.

@article{10.1109/TKDE.2005.199, 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.}, added-at = {2008-06-19T17:46:40.000+0200}, address = {Los Alamitos, CA, USA}, author = {Thie, Claire J. and Giraud-Carrier, Christophe}, biburl = {https://www.bibsonomy.org/bibtex/2ba3474451cf7c0884c0ef24fbc8dc43f/brazovayeye}, interhash = {1465071f688fa43e14673366db31b7e9}, intrahash = {ba3474451cf7c0884c0ef24fbc8dc43f}, issn = {1041-4347}, journal = {IEEE Transactions on Knowledge and Data Engineering}, keywords = {Concept STGP, algorithms, evolutionary genetic learning, programming programming, typed}, notes = {Claire Julia Kennedy}, number = 12, pages = {1664--1677}, publisher = {IEEE Computer Society}, timestamp = {2008-06-19T17:53:07.000+0200}, title = {Learning Concept Descriptions with Typed Evolutionary Programming}, url = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2005.199}, volume = 17, year = 2005 }