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On the Impact of Imbalanced Data in Convolutional Neural Networks Performance

  • Francisco J. Pulgar
  • Antonio J. Rivera
  • Francisco Charte
  • María J. del Jesus
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10334)

Abstract

In recent years, new proposals have emerged for tackling the classification problem based on Deep Learning (DL) techniques. These proposals have shown good results in certain fields, such as image recognition. However, there are factors that must be analyzed to determine how they influence the results obtained by these new algorithms. In this paper, the classification of imbalanced data with convolutional neural networks (CNNs) is analyzed. To do this, a series of tests will be performed in which the classification of real images of traffic signals by CNNs will be performed based on data with different imbalance levels.

Keywords

Deep learning Convolutional neural network Image recognition Imbalanced dataset 

Notes

The work of F. Pulgar was supported by the University of Jaén under the Action 15: Predoctoral aids for the encouragement of the doctorate. This work was partially supported by the Spanish Ministry of Science and Technology under project TIN2015-68454-R.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Francisco J. Pulgar
    • 1
  • Antonio J. Rivera
    • 1
  • Francisco Charte
    • 1
  • María J. del Jesus
    • 1
  1. 1.Depart of Computer ScienceUniversity of JaénJaénSpain

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