A fuzzy data augmentation technique to improve regularisation

International Journal of Intelligent SystemsVolume 37, Issue 8 p. 4561-4585 RESEARCH ARTICLE A fuzzy data augmentation technique to improve regularisation Rukshima Dabare, Rukshima Dabare orcid.org/0000-0002-3584-8402 Discipline of Information Technology Murdoch University, Perth, Western Australia, Australia Department of Mechanical Engineering Faculty of Engineering Technology, The Open University of Sri Lanka, Nawala, Nugegoda, Sri LankaSearch for more papers by this authorKok Wai Wong, Corresponding Author Kok Wai Wong [email protected] orcid.org/0000-0001-8767-1031 Discipline of Information Technology Murdoch University, Perth, Western Australia, Australia Correspondence Kok Wai Wong, Discipline of Information Technology, Murdoch University, South St, Murdoch, WA 6155, Australia. Email: [email protected]Search for more papers by this authorMohd Fairuz Shiratuddin, Mohd Fairuz Shiratuddin orcid.org/0000-0002-9529-6320 Discipline of Information Technology Murdoch University, Perth, Western Australia, AustraliaSearch for more papers by this authorPolychronis Koutsakis, Polychronis Koutsakis orcid.org/0000-0002-4168-0888 Discipline of Information Technology Murdoch University, Perth, Western Australia, AustraliaSearch for more papers by this author Rukshima Dabare, Rukshima Dabare orcid.org/0000-0002-3584-8402 Discipline of Information Technology Murdoch University, Perth, Western Australia, Australia Department of Mechanical Engineering Faculty of Engineering Technology, The Open University of Sri Lanka, Nawala, Nugegoda, Sri LankaSearch for more papers by this authorKok Wai Wong, Corresponding Author Kok Wai Wong [email protected] orcid.org/0000-0001-8767-1031 Discipline of Information Technology Murdoch University, Perth, Western Australia, Australia Correspondence Kok Wai Wong, Discipline of Information Technology, Murdoch University, South St, Murdoch, WA 6155, Australia. Email: [email protected]Search for more papers by this authorMohd Fairuz Shiratuddin, Mohd Fairuz Shiratuddin orcid.org/0000-0002-9529-6320 Discipline of Information Technology Murdoch University, Perth, Western Australia, AustraliaSearch for more papers by this authorPolychronis Koutsakis, Polychronis Koutsakis orcid.org/0000-0002-4168-0888 Discipline of Information Technology Murdoch University, Perth, Western Australia, AustraliaSearch for more papers by this author First published: 08 November 2021 https://doi.org/10.1002/int.22731Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL 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. Citing Literature Volume37, Issue8August 2022Pages 4561-4585 RelatedInformation