research-article

Feature Selection Using Simulated Kalman Filter (SKF) for Prediction of Body Fat Percentage

Authors:

Nurhawani Bt Ahmad Zamri,

Thangavel Bhuvaneswari,

Nor Azlina Bt Ab. Aziz,

Nor Hidayati Bt Abdul AzizAuthors Info & Claims

ICoMS '18: Proceedings of the 2018 1st International Conference on Mathematics and Statistics

Pages 23 - 27

https://doi.org/10.1145/3274250.3274264

Published: 15 July 2018 Publication History

Abstract

Simulated Kalman Filter (SKF) algorithm is a new population-based metaheuristic optimization algorithm. SKF is driven by the estimation capability of a well-known Kalman Filter. Since it is first introduced, it has been applied to various applications. Further studies also have been made to adapt SKF towards diverse area of optimization problems. Based on previous works, SKF algorithm has shown promising results. In this paper, SKF is proposed to do a feature selection for the prediction of body fat percentage. The prevalence of overweight and obesity has increased on a global scale. Thus, various methods have been introduced to evaluate obesity. SKF provides the ability to select features that resembles the percentage of body fat in an individual. The experimental results have shown that the proposed SKF feature selector is able to find the best combination of features and performs better than Particle Swarm Optimisation (PSO) which is a state of the art metaheuristic.

References

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Cited By

İMİK ŞİMŞEK ÖALAGÖZ B(2022)A THEORETICAL INVESTIGATION ON TRAINING OF PIPE-LIKE NEURAL NETWORK BENCHMARK ARCHITECTURES AND PERFORMANCE COMPARISONS OF POPULAR TRAINING ALGORITHMSBORU-BENZERİ YAPAY SİNİR AĞI KARŞILAŞTIRMA MİMARİLERİNİN EĞİTİMİ HAKKINDA BİR TEORİK ARAŞTIRMA VE POPULAR EĞİTİM ALGORİTMALARIN PERFORMANS KARŞILAŞTIRILMALARIMühendislik Bilimleri ve Tasarım Dergisi10.21923/jesd.110477210:4(1251-1271)Online publication date: 30-Dec-2022
https://doi.org/10.21923/jesd.1104772
Abdul Aziz NIbrahim ZAb. Aziz NMohamad MKamaruzaman M(2021)A brief review of simulated Kalman Filter Algorithm – variants and applicationsF1000Research10.12688/f1000research.73242.110(1081)Online publication date: 25-Oct-2021
https://doi.org/10.12688/f1000research.73242.1

Index Terms

Feature Selection Using Simulated Kalman Filter (SKF) for Prediction of Body Fat Percentage
1. Applied computing
  1. Life and medical sciences
    1. Health informatics

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ICoMS '18: Proceedings of the 2018 1st International Conference on Mathematics and Statistics

July 2018

104 pages

ISBN:9781450365383

DOI:10.1145/3274250

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Porto

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 July 2018

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Ministry of Education Malaysia

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ICoMS 2018

ICoMS 2018: 2018 International Conference on Mathematics and Statistics

July 15 - 17, 2018

Porto, Portugal

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Cited By

İMİK ŞİMŞEK ÖALAGÖZ B(2022)A THEORETICAL INVESTIGATION ON TRAINING OF PIPE-LIKE NEURAL NETWORK BENCHMARK ARCHITECTURES AND PERFORMANCE COMPARISONS OF POPULAR TRAINING ALGORITHMSBORU-BENZERİ YAPAY SİNİR AĞI KARŞILAŞTIRMA MİMARİLERİNİN EĞİTİMİ HAKKINDA BİR TEORİK ARAŞTIRMA VE POPULAR EĞİTİM ALGORİTMALARIN PERFORMANS KARŞILAŞTIRILMALARIMühendislik Bilimleri ve Tasarım Dergisi10.21923/jesd.110477210:4(1251-1271)Online publication date: 30-Dec-2022
https://doi.org/10.21923/jesd.1104772
Abdul Aziz NIbrahim ZAb. Aziz NMohamad MKamaruzaman M(2021)A brief review of simulated Kalman Filter Algorithm – variants and applicationsF1000Research10.12688/f1000research.73242.110(1081)Online publication date: 25-Oct-2021
https://doi.org/10.12688/f1000research.73242.1

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