Feature selection using simulated Kalman filter (SKF) for prediction of body fat percentage


Abdul Aziz, Nor Hidayati and Bhuvaneswari, Thangavel and Ab Aziz, Nor Azlina and Ahmad Zamri, Nurhawani (2018) Feature selection using simulated Kalman filter (SKF) for prediction of body fat percentage. In: ICoMS 2018: Proceedings of the 2018 International Conference on Mathematics and Statistics. Association for Computing Machinery, pp. 23-27. ISBN 9781450365383

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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.

Item Type: Book Section
Uncontrolled Keywords: Kalman filtering, Simulated kalman filter, Optimization, Feature selection, Body fat percentage
Subjects: Q Science > QA Mathematics > QA299.6-433 Analysis
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 20 Jan 2021 04:42
Last Modified: 20 Jan 2021 04:42
URII: http://shdl.mmu.edu.my/id/eprint/7329


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