Journal article
A fuzzy data augmentation technique to improve regularisation
International Journal of Intelligent Systems, Vol.37(8), pp.4561-4585
2022
Abstract
Deep learning (DL) has achieved superior classification in many applications due to its capability of extracting features from the data. However, the success of DL comes with the tradeoff of possible overfitting. The bias towards the data it has seen during the training process leads to poor generalisation. One way of solving this issue is by having enough training data so that the classifier is invariant to many data patterns. In the literature, data augmentation has been used as a type of regularisation method to reduce the chance for the model to overfit. However, most of the relevant works focus on image, sound or text data. There is not much work on numerical data augmentation, although many real-world problems deal with numerical data. In this paper, we propose using a technique based on Fuzzy C-Means clustering and fuzzy membership grades. Fuzzy-related techniques are used to address the variance problem by generating new data items based on fuzzy numbers and each data item's belongings to different fuzzy clusters. This data augmentation technique is used to improve the generalisation of a Deep Neural Network that is suitable for numerical data. By combining the proposed fuzzy data augmentation technique with the Dropout regularisation technique, we manage to balance the classification model's bias-variance tradeoff. Our proposed technique is evaluated using four popular data sets and is shown to provide better regularisation and higher classification accuracy compared with popular regularisation approaches.
Details
- Title
- A fuzzy data augmentation technique to improve regularisation
- Authors/Creators
- R. Dabare (Author/Creator) - Open University of Sri LankaK.W. Wong (Author/Creator) - Murdoch UniversityM.F. Shiratuddin (Author/Creator) - Murdoch UniversityP. Koutsakis (Author/Creator) - Murdoch University
- Publication Details
- International Journal of Intelligent Systems, Vol.37(8), pp.4561-4585
- Publisher
- Wiley
- Identifiers
- 991005544497007891
- Copyright
- © 2021 Wiley Periodicals LLC
- Murdoch Affiliation
- School of Information Technology
- Language
- English
- Resource Type
- Journal article
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- 4 Electrical Engineering, Electronics & Computer Science
- 4.17 Computer Vision & Graphics
- 4.17.128 Deep Visual Recognition
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