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https://github.com/hfawaz/ijcnn19ensemble
Deep Neural Network Ensembles for Time Series Classification
https://github.com/hfawaz/ijcnn19ensemble
deep-learning deep-neural-networks ensemble time-series-classification
Last synced: 3 months ago
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Deep Neural Network Ensembles for Time Series Classification
- Host: GitHub
- URL: https://github.com/hfawaz/ijcnn19ensemble
- Owner: hfawaz
- License: gpl-3.0
- Created: 2019-03-07T12:34:32.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-08-31T14:50:05.000Z (over 1 year ago)
- Last Synced: 2023-10-20T20:13:00.271Z (over 1 year ago)
- Topics: deep-learning, deep-neural-networks, ensemble, time-series-classification
- Language: Python
- Homepage:
- Size: 307 KB
- Stars: 109
- Watchers: 10
- Forks: 36
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Deep Neural Network Ensembles for Time Series Classification
This is the companion repository for our paper also available on [ArXiv](https://arxiv.org/abs/1903.06602) titled "Deep Neural Network Ensembles for Time Series Classification". This paper has been accepted at the [IEEE International Joint Conference on Neural Networks (IJCNN) 2019](https://www.ijcnn.org/).
## Approach
## Data
The data used in this project comes from the [UCR/UEA archive](http://timeseriesclassification.com/TSC.zip), which contains the 85 univariate time series datasets.
## Code
The code is divided as follows:
* The [main.py](https://github.com/hfawaz/ijcnn19ensemble/blob/master/src/main.py) python file contains the necessary code to run all experiements.
* The [utils](https://github.com/hfawaz/ijcnn19ensemble/blob/master/src/utils/) folder contains the necessary functions to read the datasets and manipulate the data.
* The [classifiers](https://github.com/hfawaz/ijcnn19ensemble/tree/master/src/classifiers) folder contains eight python files one for each deep individual/ensemble classifier presented in our paper.
To run a model on all datasets you should issue the following command:
```
python3 main.py
```
To control which datasets and which individual/ensemble classifiers to run see the options in [constants.py](https://github.com/hfawaz/ijcnn19ensemble/blob/master/src/utils/constants.py).
You can control which algorithms to include in the ensemble by changing [this line of code](https://github.com/hfawaz/ijcnn19ensemble/blob/cb822a0783ea6bd10359348f727b8fd81ae2c131/src/classifiers/nne.py#L35).
## Prerequisites
All python packages needed are listed in [pip-requirements.txt](https://github.com/hfawaz/ijcnn19ensemble/blob/master/src/utils/pip-requirements.txt) file and can be installed simply using the pip command.
* [numpy](http://www.numpy.org/)
* [pandas](https://pandas.pydata.org/)
* [sklearn](http://scikit-learn.org/stable/)
* [scipy](https://www.scipy.org/)
* [matplotlib](https://matplotlib.org/)
* [tensorflow-gpu](https://www.tensorflow.org/)
* [keras](https://keras.io/)
* [h5py](http://docs.h5py.org/en/latest/build.html)
* [keras_contrib](https://www.github.com/keras-team/keras-contrib.git)
## Results
The following table shows the results of four ensembles, the raw results can be found [here](https://github.com/hfawaz/ijcnn19ensemble/blob/master/results/results.csv).
| | Fine-tuned FCNs | NNE | ALL | ResNets |
|--------------------------------|-----------------|--------|--------|---------|
| 50words | 66.81 | **80.00** | **80.00** | 77.14 |
| Adiac | **85.17** | **85.17** | 83.38 | 83.63 |
| ArrowHead | 84.00 | 86.29 | 86.29 | **86.86** |
| Beef | 76.67 | 76.67 | **80.00** | 76.67 |
| BeetleFly | **90.00** | 85.00 | 85.00 | 85.00 |
| BirdChicken | 90.00 | **95.00** | 85.00 | 90.00 |
| CBF | **99.78** | 99.44 | 98.56 | **99.78** |
| Car | 91.67 | **95.00** | 86.67 | 93.33 |
| ChlorineConcentration | 82.42 | 85.05 | 83.98 | **85.49** |
| CinC_ECG_torso | 85.87 | 89.71 | **92.90** | 83.55 |
| Coffee | **100.00** | **100.00** | **100.00** | **100.00** |
| Computers | 83.20 | **83.60** | 71.60 | **83.60** |
| Cricket_X | 78.97 | **82.05** | 77.95 | 81.54 |
| Cricket_Y | 79.23 | **84.36** | 78.72 | 82.05 |
| Cricket_Z | 82.05 | **83.85** | 79.49 | 82.05 |
| DiatomSizeReduction | 30.07 | 30.07 | **88.56** | 30.07 |
| DistalPhalanxOutlineAgeGroup | 71.94 | 72.66 | **76.26** | 73.38 |
| DistalPhalanxOutlineCorrect | 77.54 | 77.90 | 77.90 | **78.99** |
| DistalPhalanxTW | **71.22** | 65.47 | 67.63 | 66.19 |
| ECG200 | 89.00 | 89.00 | **92.00** | 88.00 |
| ECG5000 | 94.16 | 94.42 | **94.51** | 93.67 |
| ECGFiveDays | 99.54 | **99.88** | 99.65 | 98.61 |
| Earthquakes | 71.94 | **74.82** | **74.82** | 72.66 |
| ElectricDevices | 71.74 | **74.39** | 73.03 | 74.22 |
| FISH | 96.00 | 97.71 | 93.71 | **98.29** |
| FaceAll | **92.84** | 86.39 | 83.91 | 84.02 |
| FaceFour | 93.18 | **95.45** | 92.05 | **95.45** |
| FacesUCR | 93.95 | 95.76 | 95.51 | **95.90** |
| FordA | 90.67 | 93.70 | **94.22** | 92.56 |
| FordB | 88.04 | **92.90** | 92.33 | 92.16 |
| Gun_Point | **100.00** | **100.00** | 99.33 | 99.33 |
| Ham | 74.29 | 75.24 | 74.29 | **78.10** |
| HandOutlines | 92.70 | **95.14** | 93.78 | 93.78 |
| Haptics | 50.65 | 52.60 | 50.97 | **53.25** |
| Herring | **65.62** | 60.94 | 62.50 | 60.94 |
| InlineSkate | **40.55** | 38.36 | 38.00 | 38.55 |
| InsectWingbeatSound | 39.49 | 59.75 | **65.91** | 52.73 |
| ItalyPowerDemand | 96.11 | 96.50 | **96.89** | 96.40 |
| LargeKitchenAppliances | 89.60 | **90.93** | 83.20 | 89.60 |
| Lighting2 | **80.33** | **80.33** | 77.05 | 78.69 |
| Lighting7 | 89.04 | **90.41** | 83.56 | 83.56 |
| MALLAT | 96.93 | 96.93 | 95.44 | **97.40** |
| Meat | 91.67 | 95.00 | 93.33 | **96.67** |
| MedicalImages | 78.29 | 79.74 | **80.13** | 78.42 |
| MiddlePhalanxOutlineAgeGroup | 53.90 | 59.09 | **60.39** | 59.09 |
| MiddlePhalanxOutlineCorrect | 81.10 | 83.51 | **83.85** | 83.51 |
| MiddlePhalanxTW | 51.95 | 51.95 | **55.19** | 49.35 |
| MoteStrain | 93.37 | **93.93** | 93.45 | 93.05 |
| NonInvasiveFatalECG_Thorax1 | **96.44** | 96.39 | 95.88 | 95.01 |
| NonInvasiveFatalECG_Thorax2 | 95.73 | 96.18 | 96.54 | 95.01 |
| OSULeaf | 97.52 | **98.76** | 78.51 | 98.35 |
| OliveOil | **86.67** | **86.67** | **86.67** | **86.67** |
| PhalangesOutlinesCorrect | 83.57 | 84.27 | 83.57 | **84.97** |
| Phoneme | 32.65 | **35.13** | 30.91 | 34.81 |
| Plane | **100.00** | **100.00** | 99.05 | **100.00** |
| ProximalPhalanxOutlineAgeGroup | 84.39 | 84.88 | **85.85** | 85.37 |
| ProximalPhalanxOutlineCorrect | **92.10** | 91.75 | 90.38 | **92.10** |
| ProximalPhalanxTW | 79.51 | 77.56 | **80.98** | 78.54 |
| RefrigerationDevices | 50.40 | 53.07 | **53.33** | 52.80 |
| ScreenType | **65.07** | 62.13 | 52.27 | 62.13 |
| ShapeletSim | 86.11 | 81.11 | 70.56 | **93.89** |
| ShapesAll | 90.00 | **92.83** | 89.17 | 92.00 |
| SmallKitchenAppliances | 79.47 | **82.13** | 77.60 | 78.93 |
| SonyAIBORobotSurface | 95.84 | 94.68 | 78.04 | **96.17** |
| SonyAIBORobotSurfaceII | **98.22** | 97.69 | 88.88 | 98.11 |
| StarLightCurves | 96.78 | **97.92** | 97.79 | 97.38 |
| Strawberry | 97.84 | **98.11** | 97.57 | **98.11** |
| SwedishLeaf | **97.28** | **97.28** | 96.16 | 96.48 |
| Symbols | 95.68 | **95.88** | 91.06 | 91.56 |
| ToeSegmentation1 | 96.49 | **98.25** | 81.58 | 96.05 |
| ToeSegmentation2 | 90.77 | 92.31 | **93.08** | 91.54 |
| Trace | **100.00** | **100.00** | 98.00 | **100.00** |
| TwoLeadECG | 99.91 | **100.00** | 97.72 | **100.00** |
| Two_Patterns | 87.62 | **100.00** | **100.00** | **100.00** |
| UWaveGestureLibraryAll | 82.86 | 92.27 | **96.26** | 87.16 |
| Wine | 77.78 | 87.04 | **90.74** | 83.33 |
| WordsSynonyms | 55.96 | 66.93 | **68.97** | 62.85 |
| Worms | 76.62 | 81.82 | 62.34 | **83.12** |
| WormsTwoClass | 74.03 | **77.92** | 63.64 | **77.92** |
| synthetic_control | 98.67 | **100.00** | **100.00** | **100.00** |
| uWaveGestureLibrary_X | 76.13 | 82.10 | **83.28** | 79.51 |
| uWaveGestureLibrary_Y | 64.82 | 73.20 | **75.38** | 68.68 |
| uWaveGestureLibrary_Z | 73.12 | **78.03** | 77.41 | 76.19 |
| wafer | 99.61 | 99.84 | 99.81 | **99.90** |
| yoga | 87.10 | **89.33** | 88.57 | 88.17 |
| **Wins** | 18 | **38** | 29 | 27 |
## Critical difference diagrams
If you would like to generate these diagrams, take a look at [this code](https://github.com/hfawaz/cd-diagram)!
## Reference
If you re-use this work, please cite:
```
@InProceedings{IsmailFawaz2019deep,
Title = {Deep Neural Network Ensembles for Time Series Classification},
Author = {Ismail Fawaz, Hassan and Forestier, Germain and Weber, Jonathan and Idoumghar, Lhassane and Muller, Pierre-Alain},
booktitle = {IEEE International Joint Conference on Neural Networks},
Year = {2019}
}
```
## Acknowledgement
We would like to thank NVIDIA Corporation for the Quadro P6000 grant and the Mésocentre of Strasbourg for providing access to the cluster.