Extracting Hidden

Concept drift due to hidden changes in context complicates learning in many domains including nancial prediction, medical diagnosis, and communication network performance. Existing machine learning approaches to this problem use an incremental learning, on-line paradigm. Batch, oo-line learners tend to be ineeective in domains with hidden changes in context as they assume that the training set is homogeneous. An oo-line, meta-learning approach for the identiication of hidden context is presented. The new approach uses an existing batch learner and the process of contextual clustering to identify stable hidden contexts and the associated context speciic, locally stable concepts. The approach is broadly applicable to the extraction of context reeected in time and spatial attributes. Several algorithms for the approach are presented and evaluated. A successful application of the approach to a complex ight simulator control task is also presented.