On the Impact of Imbalanced Data in Convolutional Neural Networks Performance

被引：19

作者：

Pulgar, Francisco J. ^{[1
]}

Rivera, Antonio J. ^{[1
]}

Charte, Francisco ^{[1
]}

del Jesus, Maria J. ^{[1
]}

机构：

[1] Univ Jaen, Dept Comp Sci, Jaen, Spain

来源：

HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, HAIS 2017 | 2017年 / 10334卷

关键词：

Deep learning; Convolutional neural network; Image recognition; Imbalanced dataset;

D O I：

10.1007/978-3-319-59650-1_19

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：220 / 232

页数：13

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