Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles
Abstract— Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles. Recently, two ideas have been explored that lead to more accurate algorithms for time-series classiﬁcation < Final Year Projects 2016 > TSC. First, it has been shown that the simplest way to gain improvement on TSC problems is to transform into an alternative data space where discriminatory features are more easily detected. Second, it was demonstrated that with a single data representation, improved accuracy can be achieved through simple ensemble schemes. We combine these two principles to test the hypothesis that forming a collective of ensembles of classiﬁers on different data transformations improves the accuracy of time-series classiﬁcation. The collective contains classiﬁers constructed in the time frequency, change, and shapelet transformation domains.
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