Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61420
Title: A Fuzzy Adaptive Binary Global Learning Colonization-MLP model for Body Fat Prediction
Contributor(s): Keivanian, Farshid (author); Chiong, Raymond  (author)orcid ; Hu, Zhongyi (author)
Publication Date: 2019
DOI: 10.1109/BIOSMART.2019.8734215
Handle Link: https://hdl.handle.net/1959.11/61420
Abstract: 

Body fat prediction is a step toward addressing obesity issues. In this paper, we propose a machine learning-based prediction model incorporating a novel fuzzy adaptive global learning binary colonization method for feature selection. Two fuzzy inference systems are used to select input features more purposefully. The proposed model is validated against several well-known feature selection-based models. Experimental results show that it is able to outperform the other models in comparison on most of the performance metrics considered.

Publication Type: Conference Publication
Conference Details: BioSMART 2019: 3rd International Conference on Bio-Engineering for Smart Technologies, Paris, France, 24th - 26th April, 2019
Source of Publication: Proceedings of the 3rd International Conference on Bio-Engineering for Smart Technologies, p. 1-4
Publisher: IEEE
Place of Publication: United States of America
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Appears in Collections:Conference Publication
School of Science and Technology

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