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A Multistrategy Approach to Classifier Learning from Time Series

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A Multistrategy Approach to Classifier Learning from Time Series
We present an approach to inductive concept learning using multiple models for time series. Our objective is to improve the efficiency and accuracy of concept learning by decomposing learning tasks that admit multiple types of learning architectures and mixture estimation methods. The decomposition method adapts attribute subset selection and constructive induction (cluster definition) to define new subproblems. To these problem definitions, we can apply metricbased model selection to select from a database of learning components, thereby producing a specification for supervised learning using a mixture model. We report positive learning results using temporal artificial neural networks (ANNs), on a synthetic, multiattribute learning problem and on a real-world time series monitoring application. Keywords. multistrategy learning, time series, attribute partitioning, constructive induction, metric-based model selection, mixture estimation
William H. Hsu, Sylvian R. Ray, David C. Wilkins
Added 19 Dec 2010
Updated 19 Dec 2010
Type Journal
Year 2000
Where ML
Authors William H. Hsu, Sylvian R. Ray, David C. Wilkins
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