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Current techniques such as automated relevance determination, feature selection, and clustering tend to address the problem of constructive induction in isolated stages rather than as an integrative mechanism for transforming the data – input and output schemata – into a more tractable and efficient form. As outlined in the previous section, we address this by combining search-based combinatorial optimization, statistical validation, and hierarchical abstraction into the coherent framework of composite learning.

Such problems are referred to in this chapter as decomposable; the methods addressed are: task decomposition and subproblem definition, quantitative model selection, and construction of hierarchical mixture models for data fusion. This chapter presents an integrated, multi-strategy system for decomposition of time series classifier learning tasks. A typical application for such a system is learning to predict and classify hazardous and potentially catastrophic conditions. This prediction task is also known as crisis monitoring, a form of pattern recognition that is useful in decision support or recommender systems (Resnick & Varian, 1997) for many time-critical applications.

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