Data Mining: A Heuristic Approach

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Example text

36 Hsu Subset Selection and Partitioning Attribute subset selection, also called feature subset selection, is the task of focusing a learning algorithm’s attention on some subset of the given input attributes, while ignoring the rest (Kohavi & John, 1997). In this research, subset selection is adapted to the systematic decomposition of learning problems over heterogeneous time series. Instead of focusing a single algorithm on a single subset, the set of all input attributes is partitioned, and a specialized algorithm is focused on each subset.

Larsson, S. (1999). org. & Smyth, P. (2001). Principles of data mining. Cambridge, MA: MIT Press. Heckerman, D. (1997). Bayesian networks for data mining. Data Mining and Knowledge Discovery 1, 79-119. Jaynes, E. T. (2003). Probability theory: The logic of science. Cambridge University Press, ISBN: 0521592712. Copyright © 2003, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. , & Tomkins, A. (2001). Recommendation systems: A probabilistic analysis.

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. Examples of crisis monitoring problems in the industrial, military, agricultural, and environmental sciences are numerous.

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