Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Neural Computing and Applications
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
ECG beats classification using multiclass support vector machines with error correcting output codes
Digital Signal Processing
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Exploratory undersampling for class-imbalance learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
INSIGHT: efficient and effective instance selection for time-series classification
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
ShiftTree: an interpretable model-based approach for time series classification
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
SPO: Structure Preserving Oversampling for Imbalanced Time Series Classification
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
An empirical evaluation of bagging with different algorithms on imbalanced data
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
An efficient and simple under-sampling technique for imbalanced time series classification
Proceedings of the 21st ACM international conference on Information and knowledge management
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Mining time series data and imbalanced data are two of ten challenging problems in data mining research. Imbalanced time series classification (ITSC) involves these two challenging problems, which take place in many real world applications. In the existing research, the structure-preserving over-sampling (SOP) method has been proposed for solving the ITSC problems. It is claimed by its authors to achieve better performance than other over-sampling and state-of-the-art methods in time series classification (TSC). However, it is unclear whether an under-sampling method with various learning algorithms is more effective than over-sampling methods, e.g., SPO for ITSC, because research has shown that under-sampling methods are more effective and efficient than over-sampling methods. We propose a comparative study between an under-sampling method with various learning algorithms and over-sampling methods, e.g. SPO. Statistical tests, the Friedman test and post-hoc test are applied to determine whether there is a statistically significant difference between methods. The experimental results demonstrate that the under-sampling technique with KNN is the most effective method and can achieve results that are superior to the existing complicated SPO method for ITSC.