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2018 | OriginalPaper | Buchkapitel

Classification of High Performance Archers by Means of Bio-physiological Performance Variables via k-Nearest Neighbour Classification Model

verfasst von : Zahari Taha, Rabiu Muazu Musa, Anwar P. P. Abdul Majeed, Mohamad Razali Abdullah, Ahmad Fakhri Ab. Nasir, Mohd Hasnun Arif Hassan

Erschienen in: Intelligent Manufacturing & Mechatronics

Verlag: Springer Singapore

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Abstract

The present study classified and predicted high and low potential archers from a set of bio-physiological variables trained via a machine learning technique namely k-Nearest Neighbour (k-NN). 50 youth archers drawn from various archery programmes completed a one end archery shooting score test. Bio-physiological measurements of systolic blood pressure, diastolic blood pressure, resting respiratory rate, resting heart rate and dietary intake were taken. Multiherachical agglomerative cluster analysis was used to cluster the archers based on the variables tested into low, medium and high potential archers. Three different k-NN models namely fine, medium and coarse were trained based on the measured variables. The five-fold cross-validation technique was utilised in the present investigation. It was shown from the present study, that the utilisation of k-NN is non-trivial in the classification of the performance of the archers.

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Metadaten
Titel
Classification of High Performance Archers by Means of Bio-physiological Performance Variables via k-Nearest Neighbour Classification Model
verfasst von
Zahari Taha
Rabiu Muazu Musa
Anwar P. P. Abdul Majeed
Mohamad Razali Abdullah
Ahmad Fakhri Ab. Nasir
Mohd Hasnun Arif Hassan
Copyright-Jahr
2018
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-8788-2_33

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