Time series data classification is important in many applications. Learning temporal knowledge in time series data is challenging. In this paper we propose a novel machine learning algorithm, Feature Ensemble (FE), to learn effective subsequences of signal features distributed over time series data streams. Both the FE learning and the FE classification have been applied to an application problem. Our empirical results strongly suggest that FE learning is an effective technique for time series data classification.
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