  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.
