A Fuzzy Adaptive Binary Global Learning Colonization-MLP model for Body Fat Prediction

Farshid Keivanian; Raymond Chiong; Zhongyi Hu

doi:10.1109/BIOSMART.2019.8734215

A Fuzzy Adaptive Binary Global Learning Colonization-MLP model for Body Fat Prediction

Farshid Keivanian
, Raymond Chiong
, Zhongyi Hu

School of Science & Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 3rd International Conference on Bio-Engineering for Smart Technologies
Place of Publication	United States of America
Publisher	IEEE
Pages	1-4
ISBN (Print)	9781728135786, 9781728135779, 9781728135793
DOIs	https://doi.org/10.1109/BIOSMART.2019.8734215
Publication status	Published - 2019
Event	BioSMART 2019: 3rd International Conference on Bio-Engineering for Smart Technologies - Paris, France Duration: 24 Apr 2019 → 26 Apr 2019

Conference

Conference	BioSMART 2019: 3rd International Conference on Bio-Engineering for Smart Technologies
Country/Territory	France
City	Paris
Period	24/04/19 → 26/04/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1109/BIOSMART.2019.8734215

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@inproceedings{cde8bdce41f24b65be45b3fb50090d77,

title = "A Fuzzy Adaptive Binary Global Learning Colonization-MLP model for Body Fat Prediction",

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.",

author = "Farshid Keivanian and Raymond Chiong and Zhongyi Hu",

year = "2019",

doi = "10.1109/BIOSMART.2019.8734215",

language = "English",

isbn = "9781728135786",

pages = "1--4",

booktitle = "Proceedings of the 3rd International Conference on Bio-Engineering for Smart Technologies",

publisher = "IEEE",

note = "BioSMART 2019: 3rd International Conference on Bio-Engineering for Smart Technologies ; Conference date: 24-04-2019 Through 26-04-2019",

}

Keivanian, F, Chiong, R & Hu, Z 2019, A Fuzzy Adaptive Binary Global Learning Colonization-MLP model for Body Fat Prediction. in Proceedings of the 3rd International Conference on Bio-Engineering for Smart Technologies. IEEE, United States of America, pp. 1-4, BioSMART 2019: 3rd International Conference on Bio-Engineering for Smart Technologies, Paris, France, 24/04/19. https://doi.org/10.1109/BIOSMART.2019.8734215

A Fuzzy Adaptive Binary Global Learning Colonization-MLP model for Body Fat Prediction. / Keivanian, Farshid; Chiong, Raymond; Hu, Zhongyi.
Proceedings of the 3rd International Conference on Bio-Engineering for Smart Technologies. United States of America: IEEE, 2019. p. 1-4.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

U2 - 10.1109/BIOSMART.2019.8734215

DO - 10.1109/BIOSMART.2019.8734215

M3 - Conference contribution

SN - 9781728135786

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BT - Proceedings of the 3rd International Conference on Bio-Engineering for Smart Technologies

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ER -

A Fuzzy Adaptive Binary Global Learning Colonization-MLP model for Body Fat Prediction

Abstract

Conference

UN SDGs

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