Exploiting Context When Learning to Classify

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TypeArticle
ConferenceProceedings of the European Conference on Machine Learning (ECML-93), April 5-7, 1993., Vienna, Austria
Subjectcontext; robust classification; context-sensitive features; machine learning; robust learning
AbstractThis paper addresses the problem of classifying observations when features are context-sensitive, specifically when the testing set involves a context that is different from the training set. The paper begins with a precise definition of the problem,then general strategies are presented for enhancing the performance of classification algorithms on this type of problem.These strategies are tested on two domains. The first domain is the diagnosis of gasturbine engines. The problem is to diagnose a faulty engine in one context, such as warm weather, when the fault has previously been seen only in another context, such as cold weather. These conddomain is speech recognition. The problem is to recognize words spoken by a new speaker, not represented in the training set. For both domains, exploiting context results in substantially more accurate classification.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number35058
NPARC number8913502
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Record identifier294523ef-a629-46bd-b5cb-c65cb8857d2e
Record created2009-04-22
Record modified2016-05-09
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