---
abstract: |-
This 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 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 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.
altloc:
- http://extractor.iit.nrc.ca/publications/NRC-35058.pdf
chapter: ~
commentary: ~
commref: ~
confdates: ~
conference: European Conference on Machine Learning
confloc: 'Vienna, Austria'
contact_email: ~
creators_id: []
creators_name:
- family: Turney
given: Peter
honourific: ''
lineage: ''
date: 1993
date_type: published
datestamp: 2001-11-11
department: ~
dir: disk0/00/00/18/63
edit_lock_since: ~
edit_lock_until: ~
edit_lock_user: ~
editors_id: []
editors_name: []
eprint_status: archive
eprintid: 1863
fileinfo: /style/images/fileicons/application_pdf.png;/1863/3/NRC%2D35058.pdf
full_text_status: public
importid: ~
institution: ~
isbn: ~
ispublished: pub
issn: ~
item_issues_comment: []
item_issues_count: 0
item_issues_description: []
item_issues_id: []
item_issues_reported_by: []
item_issues_resolved_by: []
item_issues_status: []
item_issues_timestamp: []
item_issues_type: []
keywords: 'context, robust classification, context-sensitive features, machine learning, robust learning.'
lastmod: 2011-03-11 08:54:49
latitude: ~
longitude: ~
metadata_visibility: show
note: ~
number: ~
pagerange: 402-407
pubdom: FALSE
publication: ~
publisher: ~
refereed: TRUE
referencetext: |
1. D.W. Aha, D. Kibler, and M.K. Albert: Instance-based learning algorithms, Machine
Learning, 6, 37-66, 1991.
2. D. Kibler, D.W. Aha, and M.K. Albert: Instance-based prediction of real-valued attributes,
Computational Intelligence, 5, 51-57, 1989.
3. B.V. Dasarathy: Nearest Neighbor Pattern Classification Techniques, (edited collection), Los
Alamitos, CA: IEEE Press, 1991.
4. N.R. Draper and H. Smith: Applied Regression Analysis, (second edition), New York, NY:
John Wiley & Sons, 1981.
5. S.E. Fahlman and C. Lebiere: The Cascade-Correlation Learning Architecture, (technical
report), CMU-CS-90-100, Pittsburgh, PA: Carnegie-Mellon University, 1991.
6. A.J. Katz, M.T. Gately, and D.R. Collins: Robust classifiers without robust features, Neural
Computation, 2, 472-479, 1990.
7. P.D. Turney and M. Halasz: Contextual normalization applied to aircraft gas turbine engine
diagnosis, (in press), Journal of Applied Intelligence, 1993.
8. D. Deterding: Speaker Normalization for Automatic Speech Recognition, (Ph.D. thesis),
Cambridge, UK: University of Cambridge, Department of Engineering, 1989.
9. A.J. Robinson: Dynamic Error Propagation Networks, (Ph.D. thesis), Cambridge, UK: Uni-versity
of Cambridge, Department of Engineering, 1989.
10. P.M. Murphy and D.W. Aha: UCI Repository of Machine Learning Databases, Irvine, CA:
University of California, Department of Information and Computer Science, 1991.
relation_type: []
relation_uri: []
reportno: ~
rev_number: 12
series: ~
source: ~
status_changed: 2007-09-12 16:41:18
subjects:
- comp-sci-art-intel
- comp-sci-mach-learn
- comp-sci-stat-model
succeeds: ~
suggestions: ~
sword_depositor: ~
sword_slug: ~
thesistype: ~
title: Exploiting context when learning to classify
type: confpaper
userid: 2175
volume: ~