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Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"

Torna all'elenco Altra tipologia anno 2020

Altra tipologia

Tipo: Software

Titolo: PyDRO: A Python reimplementation of the Distributional Random Oversampling method for binary text classification

Anno di pubblicazione: 2020

Autori: Moreo Fernandez A.D.

Affiliazioni autori: CNR-ISTI, Pisa, Italy

Autori CNR:

  • ALEJANDRO DAVID MOREO FERNANDEZ

Lingua: inglese

Sintesi: This repo is a stand-alone (re)implementation of the Distributional Random Oversampling (DRO) method presented in SIGIR'16. The former implementation was part of the JaTeCs framework for Java. Distributional Random Oversampling (DRO) is an oversampling method to counter data imbalance in binary text classification. DRO generates new random minority-class synthetic documents by exploiting the distributional properties of the terms in the collection. The variability introduced by the oversampling method is enclosed in a latent space; the original space is replicated and left untouched.

Lingua sintesi: eng

Parole chiave:

  • Python
  • Distributional Random Oversampling
  • Imbalanced Classification
  • Binary Classification

URL: https://github.com/AlexMoreo/pydro

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