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The exponential growth of volume, variety and velocity of data is raising theneed for investigations of automated or semi-automated ways to extract usefulpatterns from the data. It requires deep expert knowledge and extensivecomputational resources to find the most appropriate mapping of learningmethods for a given problem. It becomes a challenge in the presence of numerousconfigurations of learning algorithms on massive amounts of data. So there is aneed for an intelligent recommendation engine that can advise what is the bestlearning algorithm for a dataset. The techniques that are commonly used byexperts are based on a trial and error approach evaluating and comparing anumber of possible solutions against each other, using their prior experienceon a specific domain, etc. The trial and error approach combined with theexpert's prior knowledge, though computationally and time expensive, have beenoften shown to work for stationary problems where the processing is usuallyperformed off-line. However, this approach would not normally be feasible toapply to non-stationary problems where streams of data are continuouslyarriving. Furthermore, in a non-stationary environment, the manual analysis ofdata and testing of various methods whenever there is a change in theunderlying data distribution would be very difficult or simply infeasible. Inthat scenario and within an on-line predictive system, there are several taskswhere Meta-learning can be used to effectively facilitate best recommendationsincluding 1) pre-processing steps, 2) learning algorithms or their combination,3) adaptivity mechanisms and their parameters, 4) recurring concept extraction,and 5) concept drift detection.

A Review of Meta-level Learning in the Context of Multi-component, Multi-level Evolving Prediction Systems