Exploiting context when learning to classify

Turney, Peter (1993) Exploiting context when learning to classify. [Conference Paper]

Full text available as:



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.

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:1863
Deposited By:Turney, Peter
Deposited On:11 Nov 2001
Last Modified:11 Mar 2011 08:54

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

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.


Repository Staff Only: item control page