Robust classification with context-sensitive features

Turney, Peter (1993) Robust classification with context-sensitive features. [Conference Paper]

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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.

Item Type:Conference Paper
Keywords:context, robust classification, context-sensitive features, machine learning, robust learning
Subjects:Computer Science > Artificial Intelligence
Computer Science > Machine Learning
Computer Science > Statistical Models
ID Code:1861
Deposited By:Turney, Peter
Deposited On:11 Nov 2001
Last Modified:11 Mar 2011 08:54

References in Article

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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,


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,


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.


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