Imbalanced-learn

Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of over- and under-sampling, and (iv) ensemble learning methods. The proposed toolbox depends only on numpy, scipy, and scikit-learn and is distributed under MIT license. Furthermore, it is fully compatible with scikit-learn and is part of the scikit-learn-contrib supported project. Documentation, unit tests as well as integration tests are provided to ease usage and contribution. Source code, binaries, and documentation can be downloaded from https://github.com/scikit-learn-contrib/imbalanced-learn.


References in zbMATH (referenced in 9 articles )

Showing results 1 to 9 of 9.
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  1. Bej, Saptarshi; Davtyan, Narek; Wolfien, Markus; Nassar, Mariam; Wolkenhauer, Olaf: LoRAS: an oversampling approach for imbalanced datasets (2021)
  2. Koziarski, Michał; Bellinger, Colin; Woźniak, Michał: RB-CCR: radial-based combined cleaning and resampling algorithm for imbalanced data classification (2021)
  3. Merdan, Selin; Barnett, Christine L.; Denton, Brian T.; Montie, James E.; Miller, David C.: OR practice-data analytics for optimal detection of metastatic prostate cancer (2021)
  4. Chaabane, Ikram; Guermazi, Radhouane; Hammami, Mohamed: Enhancing techniques for learning decision trees from imbalanced data (2020)
  5. Liang, Jing; Wei, Panpan; Qu, Boyang; Yu, Kunjie; Yue, Caitong; Hu, Yi; Ge, Shilei: Ensemble learning based on multimodal multiobjective optimization (2020)
  6. Mahajan, Pravar Dilip; Maurya, Abhinav; Megahed, Aly; Elwany, Alaa; Strong, Ray; Blomberg, Jeanette: Optimizing predictive precision in imbalanced datasets for actionable revenue change prediction (2020)
  7. Sage Hahn, Dekang Yuan, Wesley Thompson, Max M Owens, Nicholas Allgaier, Hugh Garavan: Brain Predictability toolbox: a Python library for neuroimaging based machine learning (2020) arXiv
  8. Bing Zhu; Zihan Gao; Junkai Zhao; Seppe K.L.M. van den Broucke: IRIC: An R library for binary imbalanced classification (2019) not zbMATH
  9. Koziarski, Michał; Wożniak, Michał: CCR: a combined cleaning and resampling algorithm for imbalanced data classification (2017)