One-vs-One classification for deep neural networks

Pornntiwa Pawara*, Emmanuel Okafor, Marc Groefsema, Sheng He, Lambert R. B. Schomaker, Marco A. Wiering*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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For performing multi-class classification, deep neural networks almost always employ a One-vs-All (OvA) classification scheme with as many output units as there are classes in a dataset. The problem of this approach is that each output unit requires a complex decision boundary to separate examples from one class from all other examples. In this paper, we propose a novel One-vs-One (OvO) classification scheme for deep neural networks that trains each output unit to distinguish between a specific pair of classes. This method increases the number of output units compared to the One-vs-All classification scheme but makes learning correct decision boundaries much easier. In addition to changing the neural network architecture, we changed the loss function, created a code matrix to transform the one-hot encoding to a new label encoding, and changed the method for classifying examples. To analyze the advantages of the proposed method, we compared the One-vs-One and One-vs-All classification methods on three plant recognition datasets (including a novel dataset that we created) and a dataset with images of different monkey species using two deep architectures. The two deep convolutional neural network (CNN) architectures, Inception-V3 and ResNet-50, are trained from scratch or pre-trained weights. The results show that the One-vs-One classification method outperforms the One-vs-All method on all four datasets when training the CNNs from scratch. However, when using the two classification schemes for fine-tuning pre-trained CNNs, the One-vs-All method leads to the best performances, which is presumably because the CNNs had been pre-trained using the One-vs-All scheme.

Original languageEnglish
Article number107528
Number of pages13
JournalPattern recognition
Publication statusPublished - Dec-2020


  • Deep learning
  • Computer vision
  • Multi-class classification
  • One-vs-One classification
  • Plant recognition

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