SPO: Structure Preserving Oversampling for Imbalanced Time Series Classification

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 unreliabl...

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Bibliographic Details
Main Authors: Hong Cao, Xiao-Li Li, Yew-Kwong Woon, See-Kiong Ng
Format: Conference Proceeding
Language:English
Subjects:
Classification algorithms
eigen regularization
Eigenvalues and eigenfunctions
imbalance
learning
Oversampling
Reliability
structure preserving
Support vector machines
SVM
time series
Time series analysis
Training
Vectors
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Summary: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.
ISSN:1550-4786
2374-8486
DOI:10.1109/ICDM.2011.137