A fuzzy adaptive binary global learning colonization-mlp model for body fat prediction

Title: A fuzzy adaptive binary global learning colonization-mlp model for body fat prediction
Creator: Keivanian, Farshid; Chiong, Raymond; Hu, Zhongyi
Relation: 2019 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART). Proceedings of 2019 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART) (Paris, France 24-26 April, 2019)
Publisher Link: http://dx.doi.org/10.1109/BIOSMART.2019.8734215
Publisher: Institue of Electrical and Electronics Engineers (IEEE)
Resource Type: conference paper
Date: 2019
Description: 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.
Subject: body fat prediction; artifical neural networks; feature selection; imperialist competitive algorithm; fuzzy inference system; SDG 3; Sustainable Development Goals
Identifier: http://hdl.handle.net/1959.13/1435915
Identifier: uon:39859
Identifier: ISBN:9781728135786
Language: eng
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