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

Authors:
Guohua Liang;Chengqi Zhang
Affiliations:
UTS, Sydney, Australia;UTS, Sydney, Australia
Venue:
Proceedings of the 21st ACM international conference on Information and knowledge management
Year:
2012

Citing 14
Cited 2

Improving Identification of Difficult Small Classes by Balancing Class Distribution

AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
A study of the behavior of several methods for balancing machine learning training data

ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
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)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets

The Journal of Machine Learning Research
Experimental perspectives on learning from imbalanced data

Proceedings of the 24th international conference on Machine learning
Querying and mining of time series data: experimental comparison of representations and distance measures

Proceedings of the VLDB Endowment
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
Time Series Classification Using Support Vector Machine with Gaussian Elastic Metric Kernel

ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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
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
An empirical study of bagging predictors for imbalanced data with different levels of class distribution

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

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

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.