Cleaning Up Mislabeled Data via Image Plotting Method and Self-Attention Module
Seung Hyun Ryu, Hyejin Lee, Chan Park, PooGyeon Park · 2022 22nd International Conference on Control, Automation and Systems (ICCAS) · 2022
This paper proposes the method for cleaning up label noise in multivariate time-series outlier data. An image plotting method is proposed to reflect the tendency of original time-series data. The image data generated from the plotting method shows effectiveness, since the data can be smoothly utilized in label noise cleaning process and easy to analyze. To verify the availability of plotted data, label noise cleasing algorithm is combined to backbone network, which can estimate original noise ratio and select the data evaluated as true outlier. In addition, self-attention module is also combined to backbone network, to induce backbone network to concentrate on outlier data in plotted data. Finally, the performance of overall algorithm is evaluated through experiment based on open dataset. Using generated data through proposed plotting method, noise ratio estimation accuracy and label noise cleaning performance are evaluated. Additionally, the comparison between the proposed algorithm and the algorithm without self-attention module is also attended.