Incremental multi-classifier learning algorithm on grid'5000 for large scale image annotation
Proceedings of the international workshop on Very-large-scale multimedia corpus, mining and retrieval
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
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Imbalanced data sets present a particular challenge to the data mining community. Often, it is the rare event that is of interest and the cost of misclassifying the rare event is higher than misclassifying the usual event. When the data is highly skewed toward the usual, it can be very difficult for a learning system to accurately detect the rare event. There have been many approaches in recent years for handling imbalanced data sets, from under-sampling the majority class to adding synthetic points to the minority class in feature space. Distances between time series are known to be non-Euclidean and nonmetric, since comparing time series requires warping in time. This fact makes it impossible to apply standard methods like SMOTE to insert synthetic data points in feature spaces. We present an innovative approach that augments the minority class by adding synthetic points in distance spaces. We then use Support Vector Machines for classification. Our experimental results on standard time series show that our synthetic points significantly improve the classification rate of the rare events, and in many cases also improves the overall accuracy of SVM.