IMBENS: Ensemble Class-imbalanced Learning in Python

• URL: http://arxiv.org/abs/2111.12776v1
• Date: Wed, 24 Nov 2021 20:14:20 GMT
• Title: IMBENS: Ensemble Class-imbalanced Learning in Python
• Authors: Zhining Liu, Zhepei Wei, Erxin Yu, Qiang Huang, Kai Guo, Boyang Yu, Zhaonian Cai, Hangting Ye, Wei Cao, Jiang Bian, Pengfei Wei, Jing Jiang and Yi Chang
• Abstract summary: imbens is an open-source Python toolbox for implementing and deploying ensemble learning algorithms on class-imbalanced data. imbens is released under the MIT open-source license and can be installed from Python Package Index (PyPI)
• Score: 26.007498723608155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for quick implementing and deploying ensemble learning algorithms on class-imbalanced data. It provides access to multiple state-of-art ensemble imbalanced learning (EIL) methods, visualizer, and utility functions for dealing with the class imbalance problem. These ensemble methods include resampling-based, e.g., under/over-sampling, and reweighting-based ones, e.g., cost-sensitive learning. Beyond the implementation, we also extend conventional binary EIL algorithms with new functionalities like multi-class support and resampling scheduler, thereby enabling them to handle more complex tasks. The package was developed under a simple, well-documented API design follows that of scikit-learn for increased ease of use. imbens is released under the MIT open-source license and can be installed from Python Package Index (PyPI). Source code, binaries, detailed documentation, and usage examples are available at https://github.com/ZhiningLiu1998/imbalanced-ensemble.

Related papers

• eipy: An Open-Source Python Package for Multi-modal Data Integration using Heterogeneous Ensembles [3.465746303617158]
  eipy is an open-source Python package for developing effective, multi-modal heterogeneous ensembles for classification. eipy provides both a rigorous, and user-friendly framework for comparing and selecting the best-performing data integration and predictive modeling methods.
  arXiv  Detail & Related papers  (2024-01-17T20:07:47Z)
• BackboneLearn: A Library for Scaling Mixed-Integer Optimization-Based Machine Learning [0.0]
  BackboneLearn is a framework for scaling mixed-integer optimization problems with indicator variables to high-dimensional problems. BackboneLearn is built in Python and is user-friendly and easily implementable. The source code of BackboneLearn is available on GitHub.
  arXiv  Detail & Related papers  (2023-11-22T21:07:45Z)
• LCE: An Augmented Combination of Bagging and Boosting in Python [45.65284933207566]
  lcensemble is a high-performing, scalable and user-friendly Python package for the general tasks of classification and regression. Local Cascade Ensemble (LCE) is a machine learning method that further enhances the prediction performance of the current state-of-the-art methods Random Forest and XGBoost.
  arXiv  Detail & Related papers  (2023-08-14T16:34:47Z)
• Causal-learn: Causal Discovery in Python [53.17423883919072]
  Causal discovery aims at revealing causal relations from observational data. $textitcausal-learn$ is an open-source Python library for causal discovery.
  arXiv  Detail & Related papers  (2023-07-31T05:00:35Z)
• hyperbox-brain: A Toolbox for Hyperbox-based Machine Learning Algorithms [9.061408029414455]
  hyperbox-brain is an open-source Python library implementing the leading hyperbox-based machine learning algorithms. hyperbox-brain exposes a unified API which closely follows and is compatible with the renowned scikit-learn and numpy toolboxes.
  arXiv  Detail & Related papers  (2022-10-06T06:40:07Z)
• Solo-learn: A Library of Self-supervised Methods for Visual Representation Learning [83.02597612195966]
  solo-learn is a library of self-supervised methods for visual representation learning. Implemented in Python, using Pytorch and Pytorch lightning, the library fits both research and industry needs.
  arXiv  Detail & Related papers  (2021-08-03T22:19:55Z)
• Small-Text: Active Learning for Text Classification in Python [23.87081733039124]
  small-text is an easy-to-use active learning library for Python. It offers pool-based active learning for single- and multi-label text classification.
  arXiv  Detail & Related papers  (2021-07-21T19:23:56Z)
• BOML: A Modularized Bilevel Optimization Library in Python for Meta Learning [52.90643948602659]
  BOML is a modularized optimization library that unifies several meta-learning algorithms into a common bilevel optimization framework. It provides a hierarchical optimization pipeline together with a variety of iteration modules, which can be used to solve the mainstream categories of meta-learning methods.
  arXiv  Detail & Related papers  (2020-09-28T14:21:55Z)
• Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python [77.33905890197269]
  We describe a new library which implements a unified pathwise coordinate optimization for a variety of sparse learning problems. The library is coded in R++ and has user-friendly sparse experiments.
  arXiv  Detail & Related papers  (2020-06-27T02:39:24Z)
• mvlearn: Multiview Machine Learning in Python [103.55817158943866]
  mvlearn is a Python library which implements the leading multiview machine learning methods. The package can be installed from Python Package Index (PyPI) and the conda package manager.
  arXiv  Detail & Related papers  (2020-05-25T02:35:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.