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dc.creatorIlham, Ahmad
dc.creatorSilva, Jesús
dc.creatorMercado Caruso, Nohora Nubia
dc.creatorTapias, Donato
dc.creatorPineda, Omar
dc.description.abstractImage classification is the process of assigning an image one or multiple tags that describe its content. To perform the classification, a model must be designed for learning the labels to be assigned to a given image. The assignment is made through a learning process that uses a set of previously labeled training images, which must be large enough to guarantee efficient training. Many approaches have been researched to find optimal solutions to classification problems, however, databases with large amounts of images and the increased processing power of GPUs have made convolutional neural networks (CNNs) the best choice, as they outperform traditional algorithms. This paper presents a systematic analysis aimed at understanding how the issue of class inequality affects the efficiency of a convolutionary neural network trained for a task of image classification, and presents a technique for correcting the overtraining and that the network
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.sourceAdvances in Intelligent Systems and Computingspa
dc.subjectConvolutional neural networkspa
dc.subjectImpact of class imbalancespa
dc.subjectMulti-class problemsspa
dc.titleImpact of class imbalance on convolutional neural network training in multi-class problemsspa
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Attribution-NonCommercial-NoDerivatives 4.0 International
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