---
abstract: "This 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. The second domain 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 in substantially more accurate classification. \n\n"
altloc:
  - http://extractor.iit.nrc.ca/publications/NRC-35074.pdf
chapter: ~
commentary: ~
commref: ~
confdates: ~
conference: Sixth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems
confloc: 'Edinburgh, Scotland'
contact_email: ~
creators_id: []
creators_name:
  - family: Turney
    given: Peter
    honourific: ''
    lineage: ''
date: 1993
date_type: published
datestamp: 2001-11-11
department: ~
dir: disk0/00/00/18/61
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editors_id: []
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eprint_status: archive
eprintid: 1861
fileinfo: /style/images/fileicons/application_pdf.png;/1861/3/NRC%2D35074.pdf
full_text_status: public
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keywords: 'context, robust classification, context-sensitive features, machine learning, robust learning'
lastmod: 2011-03-11 08:54:48
latitude: ~
longitude: ~
metadata_visibility: show
note: ~
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pagerange: 268-276
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refereed: TRUE
referencetext: |-
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  7. Turney, P.D. and Halasz, M., “Contextual normaliza-tion
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  8. Deterding, D., Speaker Normalization for Automatic
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relation_type: []
relation_uri: []
reportno: ~
rev_number: 12
series: ~
source: ~
status_changed: 2007-09-12 16:41:17
subjects:
  - comp-sci-art-intel
  - comp-sci-mach-learn
  - comp-sci-stat-model
succeeds: ~
suggestions: ~
sword_depositor: ~
sword_slug: ~
thesistype: ~
title: Robust classification with context-sensitive features
type: confpaper
userid: 2175
volume: ~
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