Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Feature Subset Selection and Feature Ranking for Multivariate Time Series
IEEE Transactions on Knowledge and Data Engineering
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Time series shapelets: a new primitive for data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Early prediction on time series: a nearest neighbor approach
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A brief survey on sequence classification
ACM SIGKDD Explorations Newsletter
Margin-based over-sampling method for learning from imbalanced datasets
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Early fault classification in dynamic systems using case-based reasoning
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
SPO: Structure Preserving Oversampling for Imbalanced Time Series Classification
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Semi-supervised learning for imbalanced sentiment classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Building decision trees for the multi-class imbalance problem
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - 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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Multivariate time series (MTS) classification is an important topic in time series data mining, and lots of efficient models and techniques have been introduced to cope with it. However, early classification on imbalanced MTS data largely remains an open problem. To deal with this issue, we adopt a multiple under-sampling and dynamical subspace generation method to obtain initial training data, and each training data is used to learn a base learner. Finally, an ensemble classifier is introduced for early classification on imbalanced MTS data. Experimental results show that our proposed methods can achieve effective early prediction on imbalanced MTS data.