Distance Metric Learning with Prototype Selection for Imbalanced Classification

  1. Juan Luis Suárez 1
  2. Salvador García 1
  3. Francisco Herrera 1
  1. 1 Universidad de Granada
    info

    Universidad de Granada

    Granada, España

    ROR https://ror.org/04njjy449

Libro:
Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings
  1. Hugo Sanjurjo González (coord.)
  2. Iker Pastor López (coord.)
  3. Pablo García Bringas (coord.)
  4. Héctor Quintián (coord.)
  5. Emilio Corchado (coord.)

Editorial: Springer International Publishing AG

ISBN: 978-3-030-86271-8,  978-3-030-86270-1

Año de publicación: 2021

Páginas: 391-402

Congreso: Hybrid Artificial Intelligent Systems (HAIS) (16. 2021. Bilbao)

Tipo: Aportación congreso

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Resumen

Distance metric learning is a discipline that has recently become popular, due to its ability to significantly improve similarity based learning methods, such as the nearest neighbors classifier. Most proposals related to this topic focus on standard supervised learning and weak-supervised learning problems. In this paper, we propose a distance metric learning method to handle imbalanced classification via prototype selection. Our method, which we have called condensed neighborhood components analysis (CNCA), is an improvement of the classic neighborhood components analysis, to which foundations of the condensed nearest neighbors undersampling method are added. We show how to implement this algorithm, and provide a Python implementation. We have also evaluated its performance over imbalanced classification problems, resulting in very good performance using several imbalanced score metrics.

Fuente de los datos: Dialnet