creators_name: Turney, Peter
type: confpaper
datestamp: 2001-11-11
lastmod: 2011-03-11 08:54:48
metadata_visibility: show
title: Robust classification with context-sensitive features
ispublished: pub
subjects: comp-sci-art-intel
subjects: comp-sci-mach-learn
subjects: comp-sci-stat-model
full_text_status: public
keywords: context, robust classification, context-sensitive features, machine learning, robust learning
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. 


date: 1993
date_type: published
pagerange: 268-276
refereed: TRUE
referencetext: 1. Aha, D.W., Kibler, D., and Albert, M.K., “Instance-based
learning algorithms”, Machine Learning, 6, pp. 37-66, 1991.
2. Kibler, D., Aha, D.W., and Albert, M.K., “Instance-based
prediction of real-valued attributes”, Computational
Intelligence, 5, pp. 51-57, 1989.
3. Dasarathy, B.V. Nearest Neighbor Pattern Classifica-tion
Techniques, (edited collection), Los Alamitos,
CA: IEEE Press, 1991.
4. Draper, N.R. and Smith, H., Applied Regression
Analysis, (second edition), New York, NY: John
Wiley & Sons, 1981.
5. Fahlman, S.E. and Lebiere, C., The Cascade-Correla-tion
Learning Architecture, (technical report), CMU-CS-
90-100, Pittsburgh, PA: Carnegie-Mellon Univer-sity,
1991.
6. Katz, A.J., Gately, M.T., and Collins, D.R., “Robust
classifiers without robust features”, Neural Computa-tion,
2, pp. 472-479, 1990.
7. Turney, P.D. and Halasz, M., “Contextual normaliza-tion
applied to aircraft gas turbine engine diagnosis”,
(in press), Journal of Applied Intelligence, 1993.
8. Deterding, D., Speaker Normalization for Automatic
Speech Recognition, (Ph.D. thesis), Cambridge, UK:
University of Cambridge, Department of Engineering,
1989.
9. Robinson, A.J., Dynamic Error Propagation
Networks, (Ph.D. thesis), Cambridge, UK: University
of Cambridge, Department of Engineering, 1989.
10. Murphy, P.M. and Aha, D.W., UCI Repository of
Machine Learning Databases, Irvine, CA: University
of California, Department of Information and
Computer Science, 1991.
11. Diaconis, P. and Efron, B., “Computer-intensive
methods in statistics”, Scientific American, 248,
(May), pp. 116-131, 1983.
12. Cestnik, G., Konenenko, I., and Bratko, I., “Assistant-86:
a knowledge-elicitation tool for sophisticated
users”, in Progress in Machine Learning, edited by I.
Bratko and N. Lavrac, pp. 31-45, Wilmslow, England:
Sigma Press, 1987.
citation:   Turney, Peter  (1993) Robust classification with context-sensitive features.  [Conference Paper]     
document_url: http://cogprints.org/1861/3/NRC-35074.pdf