On-line Metalearning in Changing Contexts: METAL(B) and METAL(IB)

Many real-world concepts are heavily contextdependent. Changes in context can produce more or less radical changes in the associated concepts. On-line concept learning in such domains requires the ability to recognize and adapt to such changes. This paper concentrates on a class of learning tasks where the domain provides explicit clues as to the current context (e.g., attributes with characteristic values). A general two-level learning model is presented that effectively adjusts to changing contexts by trying to detect (via ’meter learning’) contextual clues and using this information to focus the learning process. Context learning and detection occur during regular online learning, without separate training phases for context recognition. Two operational systems based on this model are presented that differ in the underlying learning algorithm and in the way they use contextual information: METAL(B) combines meta-learning with a Bayesian classifier, while M~-TAL(IB) based on an instance-based learning algorithm. Experiments with synthetic domains as well as a ’real-world’ problem show that the algorithms are robust in a variety of dimensions, and that rectalearning produces substantial improvement over simple object-level learning in situations with changing contexts.