An efficient and simple under-sampling technique for imbalanced time series classification

Imbalanced time series classification (TSC) involving many real-world applications has increasingly captured attention of researchers. Previous work has proposed an intelligent-structure preserving over-sampling method (SPO), which the authors claimed achieved better performance than other existing over-sampling and state-of-the-art methods in TSC. The main disadvantage of over-sampling methods is that they significantly increase the computational cost of training a classification model due to the addition of new minority class instances to balance data-sets with high dimensional features. These challenging issues have motivated us to find a simple and efficient solution for imbalanced TSC. Statistical tests are applied to validate our conclusions. The experimental results demonstrate that this proposed simple random under-sampling technique with SVM is efficient and can achieve results that compare favorably with the existing complicated SPO method for imbalanced TSC. © 2012 ACM