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- W2890805747 abstract "Time series classification research is important in data mining. However, the existing methods are not fast and accurate enough. Common classification algorithms cannot satisfy the requirements of time series classification. Since the dimensions of each time series sample may be different. Based on this character, we propose a new approach combining the feature sampling algorithm and the random forest classifier (SFSC). To test the efficiency and effectiveness of SFSC, UCR time series datasets are used for our experiments. We also discuss the performance of Dynamic Time Warping (DTW) compared with our approach. We conclude the suitable situations in which our method behaves better. Besides, We discuss the method to determine the best parameters for feature sampling method. The experiment results shows that in most cases, the SFSC algorithm behaves better than DTW and can be used for real-time query and large dataset." @default.
- W2890805747 created "2018-09-27" @default.
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- W2890805747 date "2018-01-01" @default.
- W2890805747 modified "2023-10-16" @default.
- W2890805747 title "SFSC: Segment Feature Sampling Classifier for Time Series Classification" @default.
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- W2890805747 doi "https://doi.org/10.1007/978-981-13-2203-7_25" @default.
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