Giter Site home page Giter Site logo

rsip4sh / identifying-mislabelled-instances Goto Github PK

View Code? Open in Web Editor NEW
1.0 1.0 0.0 5 KB

Partial implementation of the algorithm proposed by F. Muhlenbach et al to identify and remove mislabelled instances of noisy datasets (label noise)

Python 100.00%

identifying-mislabelled-instances's Introduction

Identifying-Mislabelled-Instances

Partial implementation of the algorithm proposed by F. Muhlenbach et al to identify and remove mislabelled instances of noisy datasets.

keywords: label noise, mislabelled instances, neighbourhood graph

Todo

  • Clean code
  • Add new options to generate a geometrical neighbourhood graph
  • Add comments
  • Complete test file ( Synthetic data )
  • Complete test file ( MNIST )

References

Muhlenbach, F., Lallich, S., & Zighed, D. A. (2004). Identifying and handling mislabelled instances. Journal of Intelligent Information Systems, 22(1), 89-109.

identifying-mislabelled-instances's People

Contributors

felipeangelimvieira avatar

Stargazers

avatar

Watchers

James Cloos avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.