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appearance move to sidebar hide from wikipedia the free encyclopedia data analysis technique part of a series on machine learning and data mining paradigms supervised learning unsupervised learning semi supervised learning self supervised learning reinforcement learning transfer learning meta learning few shot learning zero shot learning online learning batch learning curriculum learning rule based learning neuro symbolic ai neuromorphic engineering quantum machine learning problems classification generative modeling regression clustering dimensionality reduction density estimation anomaly detection data cleaning automl association rules semantic analysis structured prediction feature engineering feature learning learning to rank grammar induction ontology learning multimodal learning supervised learning classification regression apprenticeship learning decision trees ensembles bagging boosting random forest k nn linear regression naive bayes artificial neural networks logistic regression perceptron relevance vector machine rvm support vector machine svm clustering birch cure hierarchical k means fuzzy expectation maximization em dbscan optics mean shift dimensionality reduction factor analysis exploratory cca ica lda nmf pca pgd t sne sdl structured prediction graphical models bayes net conditional random field hidden markov anomaly detection ransac k nn local outlier factor isolation forest neural networks autoencoder deep learning feedforward neural network recurrent neural network lstm gru esn reservoir computing boltzmann machine restricted gan diffusion model som convolutional neural network u net lenet alexnet deepdream neural field neural radiance field physics informed neural networks transformer vision mamba spiking neural network memtransistor electrochemical ram ecram reinforcement learning q learning policy gradient sarsa temporal difference td multi agent self play learning with humans active learning crowdsourcing human in the loop mechanistic interpretability rlhf model diagnostics coefficient of determination confusion matrix learning curve roc curve mathematical foundations kernel machines bias variance tradeoff computational learning theory empirical risk minimization occam learning pac learning statistical learning vc theory topological deep learning journals and conferences aaai cvpr eccv ecml pkdd emnlp iccv neurips icml iclr ijcai ml jmlr related articles glossary of artificial intelligence list of datasets for machine learning research list of datasets in computer vision and image processing outline of machine learning v t e this article s style of writing may not reflect the encyclopedic tone used on wikipedia see wikipedia s guide to writing better articles for suggestions february 2024 learn how and when to remove this message data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data 1 2 data augmentation has important applications in bayesian analysis 3 and the technique is widely used in machine learning to reduce overfitting when training machine learning models 4 achieved by training models on several slightly modified copies of existing data synthetic oversampling techniques for traditional machine learning edit main article oversampling and undersampling in data analysis oversampling techniques for classification problems synthetic minority over sampling technique smote is a method used to address imbalanced datasets in machine learning in such datasets the number of samples in different classes varies significantly leading to biased model performance for example in a medical diagnosis dataset with 90 samples representing healthy individuals and only 10 samples representing individuals with a particular disease traditional algorithms may struggle to accurately classify the minority class smote rebalances the dataset by generating synthetic samples for the minority class for instance if there are 100 samples in the majority class and 10 in the minority class smote can create synthetic samples by randomly selecting a minority class sample and its nearest neighbors then generating new samples along the line segments joining these neighbors this process helps increase the representation of the minority class improving model performance 5 data augmentation for image classification edit when convolutional neural networks grew larger in mid 1990s there was a lack of data to use 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 6 which were complemented by so called elastic distortions in 2003 7 and the technique was widely used as of 2010s 8 data augmentation can enhance cnn performance and acts as a countermeasure against cnn profiling attacks 9 data augmentation has become fundamental in image classification enriching training dataset diversity to improve model generalization and performance the evolution of this practice has introduced a broad spectrum of techniques including geometric transformations color space adjustments and noise injection 10 geometric transformations edit geometric transformations alter the spatial properties of images to simulate different perspectives orientations and scales common techniques include affine transformation rotation rotating images by a specified degree to help models recognize objects at various angles reflection reflecting images horizontally or vertically to introduce variability in orientation translation shifting images in different directions to teach models positional invariance scaling shear mapping cropping removing sections of the image to focus on particular features or simulate closer views elastic distortion 7 morphing within the same class generating new samples by applying morphing techniques between two images belonging to the same class thereby increasing intra class diversity 11 color space transformations edit color space transformations modify the color properties of images addressing variations in lighting color saturation and contrast techniques include brightness adjustment varying the image s brightness to simulate different lighting conditions contrast adjustment changing the contrast to help models recognize objects under various clarity levels saturation adjustment altering saturation to prepare models for images with diverse color intensities color jittering randomly adjusting brightness contrast saturation and hue to introduce color variability noise injection edit injecting noise into images simulates real world imperfections teaching models to ignore irrelevant variations techniques involve gaussian noise adding gaussian noise mimics sensor noise or graininess salt and pepper noise introducing black or white pixels at random simulates sensor dust or dead pixels data augmentation for signal processing edit residual or block bootstrap can be used for time series augmentation biological signals edit 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 12 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 13 a common approach is to generate synthetic signals by re arranging components of real data lotte 14 proposed a method of artificial trial generation based on analogy where three data examples x 1 x 2 x 3 displaystyle x_ 1 x_ 2 x_ 3 provide examples and an artificial x s y n t h e t i c displaystyle x_ synthetic is formed which is to x 3 displaystyle x_ 3 what x 2 displaystyle x_ 2 is to x 1 displaystyle x_ 1 a transformation is applied to x 1 displaystyle x_ 1 to make it more similar to x 2 displaystyle x_ 2 the same transformation is then applied to x 3 displaystyle x_ 3 which generates x s y n t h e t i c displaystyle x_ synthetic 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 15 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 16 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 17 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 edit 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 18 in 2022 yang et al 18 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 edit oversampling and undersampling in data analysis surrogate data generative adversarial network variational autoencoder data pre processing convolutional neural network regularization mathematics data preparation data fusion references edit dempster a p laird n m rubin d b 1977 maximum likelihood from incomplete data via the em algorithm journal of the royal statistical society series b methodological 39 1 1 22 doi 10 1111 j 2517 6161 1977 tb01600 x archived from the original on 2022 10 10 retrieved 2024 08 28 rubin donald 1987 comment the calculation of posterior distributions by data augmentation journal of the american statistical association 82 398 doi 10 2307 2289460 jstor 2289460 archived from the original on 2024 08 07 retrieved 2024 08 28 jackman simon 2009 bayesian analysis for the social sciences john wiley sons p 236 isbn 978 0 470 01154 6 shorten connor khoshgoftaar taghi m 2019 a survey on image data augmentation for deep learning mathematics and computers in simulation 6 60 springer doi 10 1186 s40537 019 0197 0 wang shujuan dai yuntao shen jihong xuan jingxue 2021 12 15 research on expansion and classification of imbalanced data based on smote algorithm scientific reports 11 1 24039 bibcode 2021natsr 1124039w doi 10 1038 s41598 021 03430 5 issn 2045 2322 pmc 8674253 pmid 34912009 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 1 2 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 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 cagli eleonora dumas cécile prouff emmanuel 2017 convolutional neural networks with data augmentation against jitter based countermeasures profiling attacks without pre processing in fischer wieland homma naofumi eds cryptographic hardware and embedded systems ches 2017 lecture notes in computer science vol 10529 cham springer international publishing pp 45 68 doi 10 1007 978 3 319 66787 4_3 isbn 978 3 319 66787 4 s2cid 54088207 shorten connor khoshgoftaar taghi m 2019 07 06 a survey on image data augmentation for deep learning journal of big data 6 1 60 doi 10 1186 s40537 019 0197 0 issn 2196 1115 ghorbel emna ghorbel faouzi 2024 06 01 data augmentation based on shape space exploration for low size datasets application to 2d shape classification neural computing and applications 36 17 10031 10054 doi 10 1007 s00521 024 09798 5 issn 1433 3058 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 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 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 archived pdf from the original on 2023 04 03 retrieved 2022 11 05 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 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 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 1...