Variants of the synthetic minority oversampling technique (SMOTE) for imbalanced learning
Project description
SMOTE-variants
Introduction
The package implements 85 variants of the Synthetic Minority Oversampling Technique (SMOTE). Besides the implementations, an easy to use model selection framework is supplied to enable the rapid evaluation of oversampling techniques on unseen datasets.
The implemented techniques: [SMOTE] , [SMOTE_TomekLinks] , [SMOTE_ENN] , [Borderline_SMOTE1] , [Borderline_SMOTE2] , [ADASYN] , [AHC] , [LLE_SMOTE] , [distance_SMOTE] , [SMMO] , [polynom_fit_SMOTE] , [Stefanowski] , [ADOMS] , [Safe_Level_SMOTE] , [MSMOTE] , [DE_oversampling] , [SMOBD] , [SUNDO] , [MSYN] , [SVM_balance] , [TRIM_SMOTE] , [SMOTE_RSB] , [ProWSyn] , [SL_graph_SMOTE] , [NRSBoundary_SMOTE] , [LVQ_SMOTE] , [SOI_CJ] , [ROSE] , [SMOTE_OUT] , [SMOTE_Cosine] , [Selected_SMOTE] , [LN_SMOTE] , [MWMOTE] , [PDFOS] , [IPADE_ID] , [RWO_sampling] , [NEATER] , [DEAGO] , [Gazzah] , [MCT] , [ADG] , [SMOTE_IPF] , [KernelADASYN] , [MOT2LD] , [V_SYNTH] , [OUPS] , [SMOTE_D] , [SMOTE_PSO] , [CURE_SMOTE] , [SOMO] , [ISOMAP_Hybrid] , [CE_SMOTE] , [Edge_Det_SMOTE] , [CBSO] , [E_SMOTE] , [DBSMOTE] , [ASMOBD] , [Assembled_SMOTE] , [SDSMOTE] , [DSMOTE] , [G_SMOTE] , [NT_SMOTE] , [Lee] , [SPY] , [SMOTE_PSOBAT] , [MDO] , [Random_SMOTE] , [ISMOTE] , [VIS_RST] , [GASMOTE] , [A_SUWO] , [SMOTE_FRST_2T] , [AND_SMOTE] , [NRAS] , [AMSCO] , [SSO] , [NDO_sampling] , [DSRBF] , [Gaussian_SMOTE] , [kmeans_SMOTE] , [Supervised_SMOTE] , [SN_SMOTE] , [CCR] , [ANS] , [cluster_SMOTE]
Citation
The publication of this work and its derivatives is going on, please come back in a couple of days or weeks for updates.
Documentation
For a detailed documentation see http://smote-variants.readthedocs.io.
For a YouTube tutorial check https://www.youtube.com/watch?v=GSK7akQPM60
The competition
We have kicked off a competition to find the best general purpose oversampling technique. The competition is ongoing, the preliminary results are available at the page https://smote-variants.readthedocs.io/en/latest/competition.html
All the numerical results are reproducible by the 005_evaluation example script, downloading the database foldings from the link below and following the instructions in the script. Anyone is open to join the competition by implementing an oversampling technique as part of the smote_variants package. The below database foldings can be used to evaluate the technique, and compare the results to the already implemented ones. Once the code is added to a feature branch, the evaluation will be repeated by the organizers and the results added to the rankings page.
Database foldings: https://drive.google.com/open?id=1PKw1vETVUzaToomio1-RGzJ9_-buYjOW
Other downloads
If someone is interested in the results of the evaluation of 85 oversamplers on 104 imbalanced datasets, the raw and aggregated results as structured pickle files are avaialble at the below links:
Raw results: https://drive.google.com/open?id=12CfB3184nchLIwStaHhrjcQK7Ari18Mo
Aggregated results: https://drive.google.com/open?id=19JGikRYXQ6-eOxaFVrqkF64zOCiSdT-j
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