Cost Sensitive Time-Series Classification

Abstract

This paper investigates the problem of highly imbalanced time-series classification using shapelets, short patterns that best characterize the target time-series, which are highly discriminative. The current state-of-the-art approach learns generalized shapelets along with weights of the classification hyperplane via a classical cost-insensitive loss function. Cost-insensitive loss functions tend to treat different misclassification errors equally and thus, models are usually biased towards examples of majority class. The rare class (which will be referred to as positive class) is usually the important class and a false negative is always costlier than a false positive. Traditional 0–1 loss functions fail to differentiate between these two types of misclassification errors. In this paper, the generalized shapelets learning framework is extended and a cost-sensitive learning model is proposed. Instead of incorporating the misclassification cost as a prior knowledge, as was done by other published methods, we formulate a constrained optimization problem to learn the unknown misclassification costs along with the shapelets and their weights. First, we demonstrated the effectiveness of the proposed method on two case studies, with the objective to detect true alarms from life threatening cardiac arrhythmia dataset from Physionets MIMIC II repository. The results show improved true alarm detection rates over the current state-of-the-art method. Next, we compared to the state-of-the-art learning shapelet method on 16 balanced dataset from UCR time-series repository. The results show evidence that the proposed method outperforms the state-of-the-art method. Finally, we performed extensive experiments across additional 18 imbalanced time-series datasets. The results provide evidence that the proposed method achieves comparable results with the state-of-the-art sampling/non-sampling based approaches for highly imbalanced time-series datasets. However, our method is highly interpretable which is an advantage over many other methods.