Feature Weighting by Explaining Case-Based Problem Solving Episodes

  • We present a similarity criterion based on feature weighting. Feature weights are recomputed dynamically according to the performance of cases during problem solving episodes. We will also present a novel algorithm to analyze and explain the performance of the retrieved cases and to determine the features whose weights need to be recomputed. We will perform experiments and show that the integration in a feature weighting model of our similarity criterion with our analysis algorithm improves the adaptability of the retrieved cases by converging to best weights for the features over a period of multiple problem solving episodes.

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Metadaten
Author:Hector Munoz-Avila, Jochem Huellen
URN (permanent link):urn:nbn:de:hbz:386-kluedo-712
Document Type:Preprint
Language of publication:English
Year of Completion:1996
Year of Publication:1996
Publishing Institute:Technische Universität Kaiserslautern
Faculties / Organisational entities:Fachbereich Informatik
DDC-Cassification:004 Datenverarbeitung; Informatik

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