In: International Joint Conference on Neural Networks. International Joint Conference on Neural Networks (IJCNN-2021) July 18-22 Virtual abs/2102.05917 Arxiv 2021.
Abstrakt
The classification of time-series data is pivotal for streaming data and comes with many challenges. Although the amount of publicly available datasets increases rapidly, deep neural models are only exploited in a few areas. Traditional methods are still used very often compared to deep neural models. These methods get preferred in safety-critical, financial, or medical fields because of their interpretable results. However, their performance and scale-ability are limited, and finding suitable explanations for time-series classification tasks is challenging due to the concepts hidden in the numerical time-series data. Visualizing complete time-series results in a cognitive overload concerning our perception and leads to confusion. Therefore, we believe that patch-wise processing of the data results in a more interpretable representation. We propose a novel hybrid approach that utilizes deep neural networks and traditional machine learning algorithms to introduce an interpretable and scale-able time-series classification approach. Our method first performs a fine-grained classification for the patches followed by sample level classification.
@inproceedings{pub11607,
author = {
Mercier, Dominique
and
Dengel, Andreas
and
Ahmed, Sheraz
},
title = {PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification},
booktitle = {International Joint Conference on Neural Networks. International Joint Conference on Neural Networks (IJCNN-2021), July 18-22, Virtual},
year = {2021},
volume = {abs/2102.05917},
publisher = {Arxiv}
}
Deutsches Forschungszentrum für Künstliche Intelligenz German Research Center for Artificial Intelligence