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The application of k -Nearest Neighbour in the identification of high potential archers based on relative psychological coping skills variables

Zahari, Taha and Musa, Rabiu Muazu and Anwar, P. P. Abdul Majeed and Mohamad Razali, Abdullah and Muhammad Muaz, Alim and Ahmad Fakhri, Ab. Nasir (2018) The application of k -Nearest Neighbour in the identification of high potential archers based on relative psychological coping skills variables. In: IOP Conference Series: Materials Science and Engineering, International Conference on Innovative Technology, Engineering and Sciences 2018 (iCITES 2018), 1-2 March 2018 , Universiti Malaysia Pahang (UMP) Pekan Campus Library, Malaysia. pp. 1-7., 342 (012019). ISSN 1757-899X

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The present study aims at classifying and predicting high and low potential archers from a collection of psychological coping skills variables trained on different k-Nearest Neighbour (k-NN) kernels. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. Psychological coping skills inventory which evaluates the archers level of related coping skills were filled out by the archers prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed k-NN models, i.e. fine, medium, coarse, cosine, cubic and weighted kernel functions, were trained on the psychological variables. The k-means clustered the archers into high psychologically prepared archers (HPPA) and low psychologically prepared archers (LPPA), respectively. It was demonstrated that the cosine k-NN model exhibited good accuracy and precision throughout the exercise with an accuracy of 94% and considerably fewer error rate for the prediction of the HPPA and the LPPA as compared to the rest of the models. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected psychological coping skills variables examined which would consequently save time and energy during talent identification and development programme.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Cluster analysis; Kernel function; K nearest neighbours (k-NN); Standard deviation
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Manufacturing Engineering
Depositing User: Dr. Ahmad Fakhri Ab. Nasir
Date Deposited: 10 Apr 2019 02:02
Last Modified: 10 Apr 2019 03:11
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