Robust Classification With Context-Sensitive Features

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TypeArticle
ConferenceProceedings of the Sixth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE-93), Edinburgh, Scotland
Subjectcontext; robust classification; context-sensitive features; machine learning; robust learning
AbstractThis paper addresses the problem of classifying observations when features are context-sensitive, especially 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 three domains. The first domain is the diagnosis of gas turbine 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 context is given by the identity of the speaker. The problem is to recognize words spoken by a new speaker, not represented in the training set. The third domain is medical prognosis. The problem is to predict whether a patient with hepatitis will live or die. The context is the age of the patient. For all three domains, exploiting context results insubstantially more accurate classification.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number35074
NPARC number5764132
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Record identifierc90892bc-7136-48cb-8b8e-077d1ed6433a
Record created2009-03-29
Record modified2016-05-09
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