Cost-Sensitive Convolution based Neural Networks for Imbalanced Time-Series Classification

Understand

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.