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Abstract: Feature Weighting is one of the most difficult tasks when developing Case Based Reasoning applications. This complexity grows when dealing with ill-defined wide domains with a sparse case base. Moreover, most widely-used feature selection and feature weighting methods assume that features are either relevant in the whole instance space or irrelevant through-out. However, it is often the case that specific features are only relevant within the context of other features' values (i.e., feature Y... (Update)
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BibTeX entry: (Update)
@misc{ merida-campos-using,
author = "Carlos M\'erida-Campos and Emma Rollon",
title = "Using a Relevance Model for Performing Feature Weighting",
url = "citeseer.ist.psu.edu/671423.html" }
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21 Contextsensitive feature selection for lazy learners - Domingos - 1997
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12 Building probabilistic networks: where do the numbers come f.. - Druzdzel, van der Gaag et al. - 1995
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7 Learning Feature Weights from Case Order Feedback (context) - Stahl - 2080
4 Integrating Bayesian networks into knowledge-intensive CBR - Aamodt, Langseth - 1998
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3 Journal of the American Society for Information Science (context) - Identification, Optimal et al.
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1 Trading MIPS and memory for knowledge angineering (context) - Creecy, Masand et al. - 1992
1 Feature Weighting by Explaining Case-based Planning Episodes (context) - Muoz-Avila, Hullen - 1996
1 Similarity of Structured Cases in CBR (context) - Armengol, Plaza - 2002
1 Relevance and information behaviour (context) - Schamber - 1994
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