  Some deep convolutional neural networks were proposed for time-series
classification and class imbalanced problems. However, those models performed
degraded and even failed to recognize the minority class of an imbalanced
temporal sequences dataset. Minority samples would bring troubles for temporal
deep learning classifiers due to the equal treatments of majority and minority
class. Until recently, there were few works applying deep learning on
imbalanced time-series classification (ITSC) tasks. Here, this paper aimed at
tackling ITSC problems with deep learning. An adaptive cost-sensitive learning
strategy was proposed to modify temporal deep learning models. Through the
proposed strategy, classifiers could automatically assign misclassification
penalties to each class. In the experimental section, the proposed method was
utilized to modify five neural networks. They were evaluated on a large volume,
real-life and imbalanced time-series dataset with six metrics. Each single
network was also tested alone and combined with several mainstream data
samplers. Experimental results illustrated that the proposed cost-sensitive
modified networks worked well on ITSC tasks. Compared to other methods, the
cost-sensitive convolution neural network and residual network won out in the
terms of all metrics. Consequently, the proposed cost-sensitive learning
strategy can be used to modify deep learning classifiers from cost-insensitive
to cost-sensitive. Those cost-sensitive convolutional networks can be
effectively applied to address ITSC issues.
