ConvXGB: A new deep learning model for classification problems based on CNN and XGBoost

We describe a new deep learning model - Convolutional eXtreme Gradient Boosting (ConvXGB) for classification problems based on convolutional neural nets and Chen et al.'s XGBoost. As well as image data, ConvXGB also supports the general classification problems, with a data preprocessing module....

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Bibliographic Details
Published inNuclear engineering and technology Vol. 53; no. 2; pp. 522 - 531
Main Authors Thongsuwan, Setthanun, Jaiyen, Saichon, Padcharoen, Anantachai, Agarwal, Praveen
Format Journal Article
LanguageKorean
Published 2021
Subjects
Deep learning
Extreme gradient boosting
XGBoost
Machine learning
Convolutional neural network (CNN)
Classification algorithms
Pattern recognition
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Summary:We describe a new deep learning model - Convolutional eXtreme Gradient Boosting (ConvXGB) for classification problems based on convolutional neural nets and Chen et al.'s XGBoost. As well as image data, ConvXGB also supports the general classification problems, with a data preprocessing module. ConvXGB consists of several stacked convolutional layers to learn the features of the input and is able to learn features automatically, followed by XGBoost in the last layer for predicting the class labels. The ConvXGB model is simplified by reducing the number of parameters under appropriate conditions, since it is not necessary re-adjust the weight values in a back propagation cycle. Experiments on several data sets from UCL Repository, including images and general data sets, showed that our model handled the classification problems, for all the tested data sets, slightly better than CNN and XGBoost alone and was sometimes significantly better.
Bibliography:KISTI1.1003/JNL.JAKO202124452777539
ISSN:1738-5733
2234-358X