EXTRACTING HIDDEN CONTEXT

Concept drift due to hidden changes in context complicates learning in many domains including financial prediction, medical diagnosis, and c ommunication network performance. Existing machine learning approaches to this problem use an incremental learning, on-line paradigm. Batch, off-line learners tend to be ineffective in domains with hidden chang es in context as they assume that the training set is homogeneous. An off-line, meta-learning approach for the identification of hidden cont ext is presented. The new approach uses an existing batch learner and the process of contextual clustering to identify stable hidden context s and the associated context specific, locally stable concepts. The ap proach is broadly applicable to the extraction of context reflected in time and spatial attributes. Several algorithms for the approach are presented and evaluated. A successful application of the approach to a complex flight simulator control task is also presented.