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A library of tools for fuzzy rough machine learning.

Project description

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fuzzy-rough-learn

fuzzy-rough-learn is a library of fuzzy rough machine learning algorithms, extending scikit-learn.

Contents

At present, fuzzy-rough-learn contains the following algorithms:

Classifiers

  • Fuzzy Rough Nearest Neighbours (FRNN; multiclass)
  • Fuzzy Rough OVO COmbination (FROVOCO; muliclass, suitable for imbalanced data)
  • Fuzzy ROugh NEighbourhood Consensus (FRONEC; multilabel)

Preprocessors

  • Fuzzy Rough Feature Selection (FRFS)
  • Fuzzy Rough Prototype Selection (FRPS)

Utilities

  • OWA operator class
  • Nearest Neighbour search algorithm class

Documentation

The documentation is located here.

Dependencies

fuzzy-rough-learn requires python 3.7+ and the following packages:

  • scipy >= 1.1.0
  • numpy >=1.16.0
  • scikit-learn >=0.22.0

Project details


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