RLTS: Robust Learning Time-Series Shapelets
Abstract
Shapelets are time-series segments effective for classifying time-series instances. Joint learning of both classifiers and shapelets has been studied in recent years because such a method provides both superior classification performance and interpretable results. For robust learning, we introduce Self-Paced Learning (SPL) and adaptive robust losses into this method. The SPL method can assign latent instance weights by considering not only classification losses but also understandable shapelet discovery. Furthermore, the adaptive robustness introduced into feature vectors is jointly learned with shapelets, a classifier, and latent instance weights. We demonstrate the superiority of AUC and the validity of our approach on UCR time-series datasets.
Cite
Text
Yamaguchi et al. "RLTS: Robust Learning Time-Series Shapelets." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020. doi:10.1007/978-3-030-67658-2_34Markdown
[Yamaguchi et al. "RLTS: Robust Learning Time-Series Shapelets." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020.](https://mlanthology.org/ecmlpkdd/2020/yamaguchi2020ecmlpkdd-rlts/) doi:10.1007/978-3-030-67658-2_34BibTeX
@inproceedings{yamaguchi2020ecmlpkdd-rlts,
title = {{RLTS: Robust Learning Time-Series Shapelets}},
author = {Yamaguchi, Akihiro and Maya, Shigeru and Ueno, Ken},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2020},
pages = {595-611},
doi = {10.1007/978-3-030-67658-2_34},
url = {https://mlanthology.org/ecmlpkdd/2020/yamaguchi2020ecmlpkdd-rlts/}
}