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.
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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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Pulgar, F.J., Rivera, A.J., Charte, F., del Jesus, M.J. (2017). On the Impact of Imbalanced Data in Convolutional Neural Networks Performance. In: Martínez de Pisón, F., Urraca, R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2017. Lecture Notes in Computer Science(), vol 10334. Springer, Cham. https://doi.org/10.1007/978-3-319-59650-1_19
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