Usage
Check the file u_shapelet.ipynb for tutorial.
Rerun the experiment using the file run_in_parallel.py.
The datasets: uncertain-dataset.tar.gz
Results
- The accuracy of each considered method and each uncertainty level is here: all_results.csv
Critical difference diagrams of models accuracy rank(lower is better)
| Low uncertainty | Medium uncertainty | High uncertainty | |
|---|---|---|---|
| NB |  |
 |
 |
| RF |  |
 |
 |
| All |  |
 |
 |
Accuracy scatter plots of UST(UED,RF) vs others
-
Low uncertainty
-
Medium uncertainty
-
High uncertainty
Critical difference diagrams of models log loss (lower is better)
| Low uncertainty | Medium uncertainty | High uncertainty | |
|---|---|---|---|
| NB |  |
 |
 |
| RF |  |
 |
 |
| All |  |
 |
 |
Dependencies
- imbalanced-learn=0.7.0
- numpy==1.19.5
- pandas==1.2.0
- sktime==0.5.1
Cite this work
@INPROCEEDINGS{mbouopda@2020,
author={Mbouopda, Michael Franklin and Mephu Nguifo, Engelbert},
booktitle={2020 International Conference on Data Mining Workshops (ICDMW)},
title={Uncertain Time Series Classification with Shapelet Transform},
year={2020},
volume={},
number={},
pages={259-266},
doi={10.1109/ICDMW51313.2020.00044}
}