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fcbf

Categorical feature selection based on information theoretical considerations

https://github.com/m-martin-j/fcbf

Science Score: 18.0%

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Keywords

feature-selection machine-learning
Last synced: 6 months ago · JSON representation ·

Repository

Categorical feature selection based on information theoretical considerations

Basic Info
  • Host: GitHub
  • Owner: m-martin-j
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 27.3 KB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 2
  • Open Issues: 0
  • Releases: 2
Topics
feature-selection machine-learning
Created over 3 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Changelog License Citation

README.md

Fast Correlation-Based Filter

A categorical feature selection approach based on information theoretical considerations.

Implementation of the fast correlation-based filter (FCBF) proposed by Yu and Liu:

bibtex @inproceedings{inproceedings, author = {Yu, Lei and Liu, Huan}, year = {2003}, month = {01}, pages = {856-863}, title = {Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution}, volume = {2}, journal = {Proceedings, Twentieth International Conference on Machine Learning} }

Data for testing is taken from the UCI Machine Learning Repository. See also notes on the contained lung cancer dataset.

Example

```py from fcbf import fcbf, data

dataset = data.lung_cancer X = dataset.loc[:, [dataset.columns[0]] + dataset.columns[2:].tolist()] y = dataset[dataset.columns[1]].astype(int) print(X) print(y)

relevantfeatures, irrelevantfeatures, correlations = fcbf(X, y, suthreshold=0.1, base=2) print('relevantfeatures:', relevantfeatures, '(count:', len(relevantfeatures), ')') print('irrelevantfeatures:', irrelevantfeatures, '(count:', len(irrelevant_features), ')') print('correlations:', correlations) ```

Setup

Using pip, execute the following

sh pip install fcbf

Development

TODO

Contributing

TODO

License

Code is released under the MIT License. All dependencies are copyright to the respective authors and released under the respective licenses.

Owner

  • Name: MartinJ
  • Login: m-martin-j
  • Kind: user
  • Location: Karlsruhe
  • Company: @fzi-forschungszentrum-informatik

Research Scientist and Machine Learning Engineer | Working on solutions for continuously maintaining productively employed artificial intelligence

Citation (CITATION.bib) 

@inproceedings{inproceedings,
author = {Yu, Lei and Liu, Huan},
year = {2003},
month = {01},
pages = {856-863},
title = {Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution},
volume = {2},
journal = {Proceedings, Twentieth International Conference on Machine Learning}
}

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Martin Trat m****t@g****m 4

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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 29 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 3
  • Total maintainers: 1
pypi.org: fcbf

Categorical feature selection based on information theoretical considerations

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 29 Last month
Rankings
Dependent packages count: 6.6%
Average: 27.9%
Forks count: 30.5%
Dependent repos count: 30.6%
Downloads: 32.5%
Stargazers count: 39.1%
Maintainers (1)
m-martin-j
Last synced: 6 months ago

Dependencies

pyproject.toml pypi
  • numpy *
  • pandas *
  • python ^3.7
  • scipy *
poetry.lock pypi
  • numpy 1.26.4
  • pandas 2.2.2
  • python-dateutil 2.9.0.post0
  • pytz 2024.1
  • scipy 1.13.0
  • six 1.16.0
  • tzdata 2024.1