Compositional instance-based learning

Patrick Broos, Karl Branting

Research output: Contribution to conferencePaper

3 Scopus citations

Abstract

This paper proposes a new algorithm for acquisition of preference predicates by a learning apprentice, termed Compositional Instance-Based Learning (CIBL), that permits multiple instances of a preference predicate to be composed, directly exploiting the transitivity of preference predicates. In an empirical evaluation, CIBL was consistently more accurate than a 1-NN instance-based learning strategy unable to compose instances. The relative performance of CIBL and decision tree induction was found to depend upon (1) the complexity of the preference predicate being acquired and (2) the dimensionality of the feature space.

Original languageEnglish (US)
Pages651-656
Number of pages6
Publication statusPublished - Dec 1 1994
EventProceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2) - Seattle, WA, USA
Duration: Jul 31 1994Aug 4 1994

Other

OtherProceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2)
CitySeattle, WA, USA
Period7/31/948/4/94

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All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Broos, P., & Branting, K. (1994). Compositional instance-based learning. 651-656. Paper presented at Proceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2), Seattle, WA, USA, .