{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/smote/papers/2","list_of":"/method/smote","method":"SMOTE","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,156],"of":156,"counts":{"archive_papers_tagged":156,"with_a_code_link":34,"where_syntology_ran_a_sample":0,"not_listed_spam_title":0,"listed":156,"listed_where_code_ran":0,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":0,"every_run_a_failure_of_syntologys_instrument":0,"listed_with_a_run_with_no_instrument_failure":0,"listed_every_run_a_failure_of_syntologys_instrument":0,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/smote","prev":"/method/smote","next":null,"papers":[{"paper":null,"slug":"classification-of-covid-19-on-chest-x-ray","title":"Classification of COVID-19 on chest X-Ray images using Deep Learning model with Histogram Equalization and Lungs Segmentation","date":"2021-12-05","arxiv_id":"2112.02478","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-based-classification","title":"Machine Learning-Based Classification Algorithms for the Prediction of Coronary Heart Diseases","date":"2021-12-02","arxiv_id":"2112.01503","n_code_links":0,"syntology":null},{"paper":null,"slug":"imbalanced-data-preprocessing-techniques","title":"Imbalanced data preprocessing techniques utilizing local data characteristics","date":"2021-11-28","arxiv_id":"2111.14120","n_code_links":0,"syntology":null},{"paper":"/paper/synthetic-sampling-from-small-datasets-a","slug":"synthetic-sampling-from-small-datasets-a","title":"Synthetic sampling from small datasets: A modified mega-trend diffusion approach using k-nearest neighbors","date":"2021-11-14","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/solving-the-class-imbalance-problem-using-a","slug":"solving-the-class-imbalance-problem-using-a","title":"Solving the Class Imbalance Problem Using a Counterfactual Method for Data Augmentation","date":"2021-11-05","arxiv_id":"2111.03516","n_code_links":1,"syntology":null},{"paper":"/paper/boosting-anomaly-detection-using-unsupervised","slug":"boosting-anomaly-detection-using-unsupervised","title":"Boosting Anomaly Detection Using Unsupervised Diverse Test-Time Augmentation","date":"2021-10-29","arxiv_id":"2110.15700","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-empirical-evaluation-of-the-t-sne","title":"An Empirical Evaluation of the t-SNE Algorithm for Data Visualization in Structural Engineering","date":"2021-09-18","arxiv_id":"2109.08795","n_code_links":0,"syntology":null},{"paper":"/paper/oversampling-highly-imbalanced-indoor","slug":"oversampling-highly-imbalanced-indoor","title":"Oversampling Highly Imbalanced Indoor Positioning Data using Deep Generative Models","date":"2021-08-30","arxiv_id":"2108.13503","n_code_links":1,"syntology":null},{"paper":null,"slug":"dtwsse-data-augmentation-with-a-siamese","title":"DTWSSE: Data Augmentation with a Siamese Encoder for Time Series","date":"2021-08-23","arxiv_id":"2108.09885","n_code_links":0,"syntology":null},{"paper":"/paper/smotified-gan-for-class-imbalanced-pattern","slug":"smotified-gan-for-class-imbalanced-pattern","title":"SMOTified-GAN for class imbalanced pattern classification problems","date":"2021-08-06","arxiv_id":"2108.03235","n_code_links":1,"syntology":null},{"paper":"/paper/a-multi-schematic-classifier-independent","slug":"a-multi-schematic-classifier-independent","title":"A multi-schematic classifier-independent oversampling approach for imbalanced datasets","date":"2021-07-15","arxiv_id":"2107.07349","n_code_links":2,"syntology":null},{"paper":null,"slug":"speech-song-emotion-recognition-using","title":"Speech & Song Emotion Recognition Using Multilayer Perceptron and Standard Vector Machine","date":"2021-05-19","arxiv_id":"2105.09406","n_code_links":0,"syntology":null},{"paper":null,"slug":"gmote-gaussian-based-minority-oversampling","title":"GMOTE: Gaussian based minority oversampling technique for imbalanced classification adapting tail probability of outliers","date":"2021-05-09","arxiv_id":"2105.03855","n_code_links":0,"syntology":null},{"paper":"/paper/a-practical-system-based-on-cnn-blstm-network","slug":"a-practical-system-based-on-cnn-blstm-network","title":"A practical system based on CNN-BLSTM network for accurate classification of ECG heartbeats of MIT-BIH imbalanced dataset","date":"2021-05-07","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/a-novel-adaptive-minority-oversampling","slug":"a-novel-adaptive-minority-oversampling","title":"A Novel Adaptive Minority Oversampling Technique for Improved Classification in Data Imbalanced Scenarios","date":"2021-03-24","arxiv_id":"2103.13823","n_code_links":1,"syntology":null},{"paper":null,"slug":"classification-of-imbalanced-credit-scoring","title":"Classification of Imbalanced Credit scoring data sets Based on Ensemble Method with the Weighted-Hybrid-Sampling","date":"2021-02-09","arxiv_id":"2102.04721","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-method-for-handling-multi-class-imbalanced","title":"A Method for Handling Multi-class Imbalanced Data by Geometry based Information Sampling and Class Prioritized Synthetic Data Generation (GICaPS)","date":"2020-10-11","arxiv_id":"2010.05155","n_code_links":0,"syntology":null},{"paper":null,"slug":"weakly-supervised-based-oversampling-for-high","title":"Weakly Supervised-Based Oversampling for High Imbalance and High Dimensionality Data Classification","date":"2020-09-29","arxiv_id":"2009.14096","n_code_links":0,"syntology":null},{"paper":null,"slug":"gamma-distribution-based-sampling-for","title":"Gamma distribution-based sampling for imbalanced data","date":"2020-09-22","arxiv_id":"2009.10343","n_code_links":0,"syntology":null},{"paper":"/paper/social-network-analytics-for-supervised-fraud","slug":"social-network-analytics-for-supervised-fraud","title":"Social network analytics for supervised fraud detection in insurance","date":"2020-09-15","arxiv_id":"2009.08313","n_code_links":1,"syntology":null},{"paper":"/paper/conditional-wasserstein-gan-based","slug":"conditional-wasserstein-gan-based","title":"Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning","date":"2020-08-20","arxiv_id":"2008.09202","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-comparison-of-synthetic-oversampling","title":"A Comparison of Synthetic Oversampling Methods for Multi-class Text Classification","date":"2020-08-11","arxiv_id":"2008.04636","n_code_links":0,"syntology":null},{"paper":null,"slug":"explainable-artificial-intelligence-based","title":"Explainable Artificial Intelligence Based Fault Diagnosis and Insight Harvesting for Steel Plates Manufacturing","date":"2020-08-10","arxiv_id":"2008.04448","n_code_links":0,"syntology":null},{"paper":null,"slug":"cost-sensitive-multi-class-adaboost-for","title":"Cost-sensitive Multi-class AdaBoost for Understanding Driving Behavior with Telematics","date":"2020-07-06","arxiv_id":"2007.03100","n_code_links":0,"syntology":null},{"paper":null,"slug":"minority-class-oversampling-for-tabular-data","title":"Minority Class Oversampling for Tabular Data with Deep Generative Models","date":"2020-05-07","arxiv_id":"2005.03773","n_code_links":0,"syntology":null},{"paper":null,"slug":"combined-cleaning-and-resampling-algorithm","title":"Combined Cleaning and Resampling Algorithm for Multi-Class Imbalanced Data with Label Noise","date":"2020-04-07","arxiv_id":"2004.03406","n_code_links":0,"syntology":null},{"paper":null,"slug":"csmoute-combined-synthetic-oversampling-and","title":"CSMOUTE: Combined Synthetic Oversampling and Undersampling Technique for Imbalanced Data Classification","date":"2020-04-07","arxiv_id":"2004.03409","n_code_links":0,"syntology":null},{"paper":null,"slug":"two-stage-resampling-for-convolutional-neural","title":"Two-Stage Resampling for Convolutional Neural Network Training in the Imbalanced Colorectal Cancer Image Classification","date":"2020-04-07","arxiv_id":"2004.03332","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-synthetic-minority-over-sampling","title":"Deep Synthetic Minority Over-Sampling Technique","date":"2020-03-22","arxiv_id":"2003.09788","n_code_links":0,"syntology":null},{"paper":null,"slug":"computer-aided-diagnosis-for-spitzoid-lesions","title":"Spitzoid Lesions Diagnosis based on GA feature selection and Random Forest","date":"2020-03-10","arxiv_id":"2003.04745","n_code_links":0,"syntology":null},{"paper":null,"slug":"are-you-an-introvert-or-extrovert-accurate","title":"Are You an Introvert or Extrovert? Accurate Classification With Only Ten Predictors","date":"2020-02-29","arxiv_id":"2003.01580","n_code_links":0,"syntology":null},{"paper":null,"slug":"wotboost-weighted-oversampling-technique-in","title":"WOTBoost: Weighted Oversampling Technique in Boosting for imbalanced learning","date":"2019-10-17","arxiv_id":"1910.07892","n_code_links":0,"syntology":null},{"paper":null,"slug":"spam-filtering-on-forums-a-synthetic","title":"Spam filtering on forums: A synthetic oversampling based approach for imbalanced data classification","date":"2019-09-10","arxiv_id":"1909.04826","n_code_links":0,"syntology":null},{"paper":null,"slug":"minimizing-the-societal-cost-of-credit-card","title":"Minimizing the Societal Cost of Credit Card Fraud with Limited and Imbalanced Data","date":"2019-09-03","arxiv_id":"1909.01486","n_code_links":0,"syntology":null},{"paper":"/paper/loras-an-oversampling-approach-for-imbalanced","slug":"loras-an-oversampling-approach-for-imbalanced","title":"LoRAS: An oversampling approach for imbalanced datasets","date":"2019-08-22","arxiv_id":"1908.08346","n_code_links":1,"syntology":null},{"paper":"/paper/190600452","slug":"190600452","title":"Radial-Based Undersampling for Imbalanced Data Classification","date":"2019-06-02","arxiv_id":"1906.00452","n_code_links":1,"syntology":null},{"paper":null,"slug":"ssn-sparks-at-semeval-2019-task-9-mining","title":"SSN-SPARKS at SemEval-2019 Task 9: Mining Suggestions from Online Reviews using Deep Learning Techniques on Augmented Data","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/data-augmentation-using-gans","slug":"data-augmentation-using-gans","title":"Data Augmentation Using GANs","date":"2019-04-19","arxiv_id":"1904.09135","n_code_links":1,"syntology":null},{"paper":null,"slug":"predicting-class-imbalanced-business-risk","title":"Predicting class-imbalanced business risk using resampling, regularization, and model ensembling algorithms","date":"2019-03-13","arxiv_id":"1903.05535","n_code_links":0,"syntology":null},{"paper":"/paper/transforma-at-semeval-2019-task-6-offensive","slug":"transforma-at-semeval-2019-task-6-offensive","title":"Offensive Language Analysis using Deep Learning Architecture","date":"2019-03-12","arxiv_id":"1903.05280","n_code_links":1,"syntology":null},{"paper":"/paper/heartbeat-anomaly-detection-using-adversarial","slug":"heartbeat-anomaly-detection-using-adversarial","title":"Heartbeat Anomaly Detection using Adversarial Oversampling","date":"2019-01-28","arxiv_id":"1901.09972","n_code_links":1,"syntology":null},{"paper":"/paper/autoencoders-and-generative-adversarial","slug":"autoencoders-and-generative-adversarial","title":"Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification","date":"2019-01-08","arxiv_id":"1901.02514","n_code_links":1,"syntology":null},{"paper":null,"slug":"anomaly-generation-using-generative","title":"Anomaly Generation using Generative Adversarial Networks in Host Based Intrusion Detection","date":"2018-12-11","arxiv_id":"1812.04697","n_code_links":0,"syntology":null},{"paper":"/paper/licic-less-important-components-for","slug":"licic-less-important-components-for","title":"LICIC: Less Important Components for Imbalanced Multiclass Classification","date":"2018-12-09","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/benchmarking-datasets-for-anomaly-based","slug":"benchmarking-datasets-for-anomaly-based","title":"Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives","date":"2018-11-13","arxiv_id":"1811.05372","n_code_links":1,"syntology":null},{"paper":null,"slug":"malicious-web-domain-identification-using","title":"Malicious Web Domain Identification using Online Credibility and Performance Data by Considering the Class Imbalance Issue","date":"2018-10-19","arxiv_id":"1810.08359","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-empirical-evaluation-of-imbalanced-data","title":"An empirical evaluation of imbalanced data strategies from a practitioner's point of view","date":"2018-10-16","arxiv_id":"1810.07168","n_code_links":0,"syntology":null},{"paper":null,"slug":"nlprl-iitbhu-at-semeval-2018-task-3-combining","title":"NLPRL-IITBHU at SemEval-2018 Task 3: Combining Linguistic Features and Emoji pre-trained CNN for Irony Detection in Tweets","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-electricity-outages-caused-by","title":"Predicting Electricity Outages Caused by Convective Storms","date":"2018-05-21","arxiv_id":"1805.07897","n_code_links":0,"syntology":null},{"paper":null,"slug":"modified-smote-using-mutual-information-and","title":"Modified SMOTE Using Mutual Information and Different Sorts of Entropies","date":"2018-03-29","arxiv_id":"1803.11002","n_code_links":0,"syntology":null},{"paper":null,"slug":"introducing-deepbalance-random-deep-belief","title":"Introducing DeepBalance: Random Deep Belief Network Ensembles to Address Class Imbalance","date":"2017-09-28","arxiv_id":"1709.10056","n_code_links":0,"syntology":null},{"paper":null,"slug":"geometric-smote-effective-oversampling-for","title":"Geometric SMOTE: Effective oversampling for imbalanced learning through a geometric extension of SMOTE","date":"2017-09-21","arxiv_id":"1709.07377","n_code_links":0,"syntology":null},{"paper":"/paper/cgmos-certainty-guided-minority-oversampling","slug":"cgmos-certainty-guided-minority-oversampling","title":"CGMOS: Certainty Guided Minority OverSampling","date":"2016-07-21","arxiv_id":"1607.06525","n_code_links":1,"syntology":null},{"paper":null,"slug":"improved-sampling-techniques-for-learning-an","title":"Improved Sampling Techniques for Learning an Imbalanced Data Set","date":"2016-01-18","arxiv_id":"1601.04756","n_code_links":0,"syntology":null},{"paper":null,"slug":"combination-of-pca-with-smote-resampling-to","title":"Combination of PCA with SMOTE Resampling to Boost the Prediction Rate in Lung Cancer Dataset","date":"2014-03-08","arxiv_id":"1403.1949","n_code_links":0,"syntology":null},{"paper":"/paper/smote-synthetic-minority-over-sampling","slug":"smote-synthetic-minority-over-sampling","title":"SMOTE: Synthetic Minority Over-sampling Technique","date":"2011-06-09","arxiv_id":"1106.1813","n_code_links":25,"syntology":null}],"record_sha256":"a3e9da94c8340b278f084ebfb5255d966f7937ba6754faad5d7d46e277121cf8","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}