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• W2123502857 abstract "The problem of time series classification (TSC), where we consider any real-valued ordered data a time series, presents a specific machine learning challenge as the ordering of variables is often crucial in finding the best discriminating features. One of the most promising recent approaches is to find shapelets within a data set. A shapelet is a time series subsequence that is identified as being representative of class membership. The original research in this field embedded the procedure of finding shapelets within a decision tree. We propose disconnecting the process of finding shapelets from the classification algorithm by proposing a shapelet transformation. We describe a means of extracting the k best shapelets from a data set in a single pass, and then use these shapelets to transform data by calculating the distances from a series to each shapelet. We demonstrate that transformation into this new data space can improve classification accuracy, whilst retaining the explanatory power provided by shapelets." @default.
• W2123502857 created "2016-06-24" @default.
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• W2123502857 date "2012-08-12" @default.
• W2123502857 modified "2023-10-16" @default.
• W2123502857 title "A shapelet transform for time series classification" @default.
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• W2123502857 doi "https://doi.org/10.1145/2339530.2339579" @default.
• W2123502857 hasPublicationYear "2012" @default.
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