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Data augmentation is a technique in machine learning used to reduce overfitting when training a machine learning model,[1] by training models on several slightly-modified copies of existing data.

Synthetic oversampling techniques for traditional machine learning

Main article: Oversampling and undersampling in data analysis § Oversampling techniques for classification problems

Data augmentation for image classification

When convolutional neural networks grew larger in mid-1990s, there often was not enough available data to train them, especially considering that some part of the overall dataset should be spared for later testing. It was proposed to perturb existing data with affine transformations to create new examples with the same labels,[2] which were complemented by so-called elastic distortions in 2003,[3] and the technique was widely used as of 2010s.[4]

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Data augmentation for signal processing

Residual or block bootstrap can be used for time series augmentation.

Biological signals

Synthetic data augmentation is of paramount importance for machine learning classification, particularly for biological data, which tend to be high dimensional and scarce. The applications of robotic control and augmentation in disabled and able-bodied subjects still rely mainly on subject-specific analyses. Data scarcity is notable in signal processing problems such as for Parkinson's Disease Electromyography signals, which are difficult to source - Zanini, et al. noted that it is possible to use a generative adversarial network (in particular, a DCGAN) to perform style transfer in order to generate synthetic electromyographic signals that corresponded to those exhibited by sufferers of Parkinson's Disease.[5]

The approaches are also important in electroencephalography (brainwaves). Wang, et al. explored the idea of using deep convolutional neural networks for EEG-Based Emotion Recognition, results show that emotion recognition was improved when data augmentation was used.[6]

A common approach is to generate synthetic signals by re-arranging components of real data. Lotte[7] proposed a method of "Artificial Trial Generation Based on Analogy" where three data examples provide examples and an artificial is formed which is to what is to . A transformation is applied to to make it more similar to , the same transformation is then applied to which generates . This approach was shown to improve performance of a Linear Discriminant Analysis classifier on three different datasets.

Current research shows great impact can be derived from relatively simple techniques. For example, Freer[8] observed that introducing noise into gathered data to form additional data points improved the learning ability of several models which otherwise performed relatively poorly. Tsinganos et al.[9] studied the approaches of magnitude warping, wavelet decomposition, and synthetic surface EMG models (generative approaches) for hand gesture recognition, finding classification performance increases of up to +16% when augmented data was introduced during training. More recently, data augmentation studies have begun to focus on the field of deep learning, more specifically on the ability of generative models to create artificial data which is then introduced during the classification model training process. In 2018, Luo et al.[10] observed that useful EEG signal data could be generated by Conditional Wasserstein Generative Adversarial Networks (GANs) which was then introduced to the training set in a classical train-test learning framework. The authors found classification performance was improved when such techniques were introduced.

Mechanical signals

The prediction of mechanical signals based on data augmentation brings a new generation of technological innovations, such as new energy dispatch, 5G communication field, and robotics control engineering.[11] In 2022, Yang et al.[11] integrate constraints, optimization and control into a deep network framework based on data augmentation and data pruning with spatio-temporal data correlation, and improve the interpretability, safety and controllability of deep learning in real industrial projects through explicit mathematical programming equations and analytical solutions.

See also


  1. ^ Shorten, Connor; Khoshgoftaar, Taghi M. (2019). "A survey on Image Data Augmentation for Deep Learning". Mathematics and Computers in Simulation. springer. 6: 60. doi:10.1186/s40537-019-0197-0.
  2. ^ Yann Lecun; et al. (1995). Learning algorithms for classification: A comparison on handwritten digit recognition (Conference paper). World Scientific. pp. 261–276. Retrieved 14 May 2023. ((cite book)): |website= ignored (help)
  3. ^ Simard, P.Y.; Steinkraus, D.; Platt, J.C. (2003). "Best practices for convolutional neural networks applied to visual document analysis". Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings. Vol. 1. pp. 958–963. doi:10.1109/ICDAR.2003.1227801. ISBN 0-7695-1960-1. S2CID 4659176.
  4. ^ Hinton, Geoffrey E.; Srivastava, Nitish; Krizhevsky, Alex; Sutskever, Ilya; Salakhutdinov, Ruslan R. (2012). "Improving neural networks by preventing co-adaptation of feature detectors". arXiv:1207.0580 [cs.NE].
  5. ^ Anicet Zanini, Rafael; Luna Colombini, Esther (2020). "Parkinson's Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer". Sensors. 20 (9): 2605. Bibcode:2020Senso..20.2605A. doi:10.3390/s20092605. ISSN 1424-8220. PMC 7248755. PMID 32375217.
  6. ^ Wang, Fang; Zhong, Sheng-hua; Peng, Jianfeng; Jiang, Jianmin; Liu, Yan (2018). "Data Augmentation for EEG-Based Emotion Recognition with Deep Convolutional Neural Networks". MultiMedia Modeling. Lecture Notes in Computer Science. Vol. 10705. pp. 82–93. doi:10.1007/978-3-319-73600-6_8. ISBN 978-3-319-73599-3. ISSN 0302-9743.
  7. ^ Lotte, Fabien (2015). "Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces" (PDF). Proceedings of the IEEE. 103 (6): 871–890. doi:10.1109/JPROC.2015.2404941. ISSN 0018-9219. S2CID 22472204.
  8. ^ Freer, Daniel; Yang, Guang-Zhong (2020). "Data augmentation for self-paced motor imagery classification with C-LSTM". Journal of Neural Engineering. 17 (1): 016041. Bibcode:2020JNEng..17a6041F. doi:10.1088/1741-2552/ab57c0. hdl:10044/1/75376. ISSN 1741-2552. PMID 31726440. S2CID 208034533.
  9. ^ Tsinganos, Panagiotis; Cornelis, Bruno; Cornelis, Jan; Jansen, Bart; Skodras, Athanassios (2020). "Data Augmentation of Surface Electromyography for Hand Gesture Recognition". Sensors. 20 (17): 4892. Bibcode:2020Senso..20.4892T. doi:10.3390/s20174892. ISSN 1424-8220. PMC 7506981. PMID 32872508.
  10. ^ Luo, Yun; Lu, Bao-Liang (2018). "EEG Data Augmentation for Emotion Recognition Using a Conditional Wasserstein GAN". 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Vol. 2018. pp. 2535–2538. doi:10.1109/EMBC.2018.8512865. ISBN 978-1-5386-3646-6. PMID 30440924. S2CID 53105445.
  11. ^ a b Yang, Yang (2022). "Wind speed forecasting with correlation network pruning and augmentation: A two-phase deep learning method". Renewable Energy. 198 (1): 267–282. arXiv:2306.01986. doi:10.1016/j.renene.2022.07.125. ISSN 0960-1481. S2CID 251511199.