Software:scikit-multiflow
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| Original author(s) | Jacob Montiel, Jesse Read, Albert Bifet, Talel Abdessalem |
|---|---|
| Developer(s) | The scikit-mutliflow development team and the open research community |
| Initial release | January 2018 |
| Stable release | |
| Repository | https://github.com/scikit-multiflow/scikit-multiflow |
| Written in | Python, Cython |
| Operating system | Linux, macOS, Windows |
| Type | Library for machine learning |
| License | BSD 3-Clause license |
| Website | scikit-multiflow |
scikit-mutliflow (also known as skmultiflow) is a free and open source software machine learning library for multi-output/multi-label and stream data written in Python.[3]
Overview
scikit-multiflow allows to easily design and run experiments and to extend existing stream learning algorithms.[3] It features a collection of classification, regression, concept drift detection and anomaly detection algorithms. It also includes a set of data stream generators and evaluators. scikit-multiflow is designed to interoperate with Python's numerical and scientific libraries NumPy and SciPy and is compatible with Jupyter Notebooks.
Implementation
The scikit-multiflow library is implemented under the open research principles and is currently distributed under the BSD 3-Clause license. scikit-multiflow is mainly written in Python, and some core elements are written in Cython for performance. scikit-multiflow integrates with other Python libraries such as Matplotlib for plotting, scikit-learn for incremental learning methods[4] compatible with the stream learning setting, Pandas for data manipulation, Numpy and SciPy.
Components
The scikit-multiflow is composed of the following sub-packages:
- anomaly_detection: anomaly detection methods.
- data: data stream methods including methods for batch-to-stream conversion and generators.
- drift_detection: methods for concept drift detection.
- evaluation: evaluation methods for stream learning.
- lazy: methods in which generalisation of the training data is delayed until a query is received, i.e., neighbours-based methods such as kNN.
- meta: meta learning (also known as ensemble) methods.
- neural_networks: methods based on neural networks.
- prototype: prototype-based learning methods.
- rules: rule-based learning methods.
- transform: perform data transformations.
- trees: tree-based methods, e.g. Hoeffding Trees which are a type of Decision Tree for data streams.
History
scikit-multiflow started as a collaboration between researchers at Télécom Paris (Institut Polytechnique de Paris[5]) and École Polytechnique. Development is currently carried by the University of Waikato, Télécom Paris, École Polytechnique and the open research community.
See also
- Massive Online Analysis (MOA)[6]
- MEKA[7]
References
- ↑ "scikit-mutliflow Version 0.5.3". https://scikit-multiflow.readthedocs.io/en/stable/whats_new.html#version-0-5-0.
- ↑ "scikit-learn 0.5.3". https://pypi.org/project/scikit-multiflow/0.5.3/.
- ↑ 3.0 3.1 Montiel, Jacob; Read, Jesse; Bifet, Albert; Abdessalem, Talel (2018). "Scikit-Multiflow: A Multi-output Streaming Framework". Journal of Machine Learning Research 19 (72): 1–5. ISSN 1533-7928. http://jmlr.org/papers/v19/18-251.html.
- ↑ "scikit-learn — Incremental learning". https://scikit-learn.org/stable/modules/computing.html?highlight=incremental#incremental-learning.
- ↑ "Institut Polytechnique de Paris" (in en-GB). https://www.ip-paris.fr/en/home-en/.
- ↑ Bifet, Albert; Holmes, Geoff; Kirkby, Richard; Pfahringer, Bernhard (2010). "MOA: Massive Online Analysis". Journal of Machine Learning Research 11 (52): 1601–1604. ISSN 1533-7928. http://jmlr.org/papers/v11/bifet10a.html.
- ↑ Read, Jesse; Reutemann, Peter; Pfahringer, Bernhard; Holmes, Geoff (2016). "MEKA: A Multi-label/Multi-target Extension to WEKA". Journal of Machine Learning Research 17 (21): 1–5. ISSN 1533-7928. http://jmlr.org/papers/v17/12-164.html.
External links
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