A Coupling Support Vector Machines with the Feature Learning of Deep Convolutional Neural Networks for Classifying Microarray Gene Expression Data

  • Phuoc-Hai HuynhEmail author
  • Van-Hoa NguyenEmail author
  • Thanh-Nghi DoEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 769)


Support vector machines (SVM) and deep convolutional neural networks (DCNNs) are state-of-the-art classification techniques in many real-world applications. Our investigation aims at proposing a hybrid model combining DCNNs and SVM (called DCNN-SVM) to effectively predict very-high-dimensional gene expression data. The DCNN-SVM trains the DCNNs model to automatically extract features from microarray gene expression data and followed which the DCNN-SVM learns a non-linear SVM model to classify gene expression data. Numerical test results on 15 microarray datasets from Array Expression and Medical Database (Kent Ridge) show that our proposed DCNN-SVM is more accurate than the classical DCNNs algorithm, SVM, random forests.


Microarray gene expression Convolutional neural networks Support vector machines 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.An Giang UniversityAn GiangVietnam
  2. 2.Can Tho UniversityCan ThoVietnam

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