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

TL;DR

This paper introduces an adaptive cost-sensitive learning strategy to improve deep convolutional neural networks for imbalanced time-series classification, significantly enhancing minority class recognition in real-world datasets.

Contribution

It proposes a novel cost-sensitive modification method for deep learning models, effectively addressing class imbalance in time-series classification tasks.

Findings

Cost-sensitive CNNs outperform traditional models on imbalanced datasets.

The strategy improves minority class recognition without sacrificing overall accuracy.

Modified networks show robustness across multiple metrics and data sampling methods.

Abstract

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.