Computer Science – Learning
Scientific paper
2002-12-12
Proceedings of the Sixth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Ex
Computer Science
Learning
9 pages
Scientific paper
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.
No associations
LandOfFree
Robust Classification with Context-Sensitive Features does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Robust Classification with Context-Sensitive Features, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Robust Classification with Context-Sensitive Features will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-404283