A Knowledge-based Recommendation System for Time Series Classification
Time series data sets reflect the state and extent of things as they change over time. Information extraction based on such data plays an important role in many fields. The time series classification is a typical supervised learning problem, which is applied in speech recognition, image processing a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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FRUCT
2019-04-01
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| Series: | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| Subjects: | |
| Online Access: | https://fruct.org/publications/abstract24/files/Tia.pdf
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| Summary: | Time series data sets reflect the state and extent of things as they change over time. Information extraction based on such data plays an important role in many fields. The time series classification is a typical supervised learning problem, which is applied in speech recognition, image processing and so on. However, because the attributes of time series data don't make sense and the feature dimensions are particularly large, people can't treat them as general machine learning classification problems. Currently, many different time series classification problems have been proposed. But how to choose and use these methods is still a huge problem for non-computer professional researchers. This article uses the ontology technology to build a recommendation system that contains the details and features of such algorithms. When the users input the characteristics of the data and the task requirements, they can get reasonable suggestions and a description of the workflow of the algorithm. Such a system saves the user a lot of analysis and comparison time. It also makes such problems easier to understand. |
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| ISSN: | 2305-7254 2343-0737 |