Publication: A Machine Learning Framework for Balancing Training Sets of Sensor Sequential Data Streams
A Machine Learning Framework for Balancing Training Sets of Sensor Sequential Data Streams
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Setiawan, B. D., Serdült, U., & Kryssanov, V. (2021). A Machine Learning Framework for Balancing Training Sets of Sensor Sequential Data Streams. Sensors, 21(20), 6892. https://doi.org/10.3390/s21206892
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The recent explosive growth in the number of smart technologies relying on data collected from sensors and processed with machine learning classifiers made the training data imbalance problem more visible than ever before. Class-imbalanced sets used to train models of various events of interest are among the main reasons for a smart technology to work incorrectly or even to completely fail. This paper presents an attempt to resolve the imbalance problem in sensor sequential (time-series) data through training data augmentation. An Unr
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- Setiawan, Budi Darma
- Kryssanov, Victor
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Setiawan, B. D., Serdült, U., & Kryssanov, V. (2021). A Machine Learning Framework for Balancing Training Sets of Sensor Sequential Data Streams. Sensors, 21(20), 6892. https://doi.org/10.3390/s21206892
