Cost-sensitive convolutional neural networks for imbalanced time series classification

Article type: Research Article

Authors: Geng, Yue^* | Luo, Xinyu

Affiliations: Mechanical and Electrical Engineering Institute, China University of Mining and Technology Beijing, Beijing, China

Correspondence: [*] Corresponding author: Yue Geng, Mechanical and Electrical Engineering Institute, China University of Mining and Technology Beijing, Beijing 100083, China. E-mail: [email protected].

Abstract: Time series classification and class imbalance problem are two common issues in a multitude of real-life scenarios. This paper simultaneously explores both issues with deep convolution neural networks (CNNs). Because standard networks treat the majority and minority classes with same class weights, most CNN-based networks fail to classify imbalanced time series. Until recently, there is very little work applying deep learning to imbalanced time series classification (ITSC). Thus, we propose an adaptive cost-sensitive learning strategy to address the ITSC problem. The standard CNN is modified to a cost-sensitive network (CS-CNN), which is able to punish the misclassified samples using a class-dependent cost matrix. Moreover, this cost matrix is automatically updated based on overall class distribution and the CS-CNN’s training performance. The proposed method is extended to FCN, LSTM-FCN and ResNet. It is experimentally tested on five public benchmark UCR datasets and a real-life large volume dataset. Four cost-sensitive CNN-based networks are compared with several data samplers and two traditional ITSC methods. The modified networks are superior in all metrics. Results show that cost-sensitive networks successfully complete the ITSC tasks.

Keywords: Convolutional neural networks, class imbalance problems, cost-sensitive learning, imbalanced time series classification

DOI: 10.3233/IDA-183831

Journal: Intelligent Data Analysis, vol. 23, no. 2, pp. 357-370, 2019