A Multistrategy Approach to Classifier Learning from Time Series

Abstract

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 metric-based 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.