An integrated framework for human activity classification
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
An integrated framework for human activity recognition
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
A comparative study of sampling methods and algorithms for imbalanced time series classification
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Early prediction on imbalanced multivariate time series
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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This paper presents a novel structure preserving over sampling (SPO) technique for classifying imbalanced time series data. SPO generates synthetic minority samples based on multivariate Gaussian distribution by estimating the covariance structure of the minority class and regularizing the unreliable eigen spectrum. By preserving the main covariance structure and intelligently creating protective variances in the trivial eigen feature dimensions, the synthetic samples expand effectively into the void area in the data space without being too closely tied with existing minority-class samples. Extensive experiments based on several public time series datasets demonstrate that our proposed SPO in conjunction with support vector machines can achieve better performances than existing over sampling methods and state-of-the-art methods in time series classification.