Abstract
This paper presents a comparative case study on the classification accuracy between five methods for golf putting performance analysis. In a previous work, a digital camera was used to capture 30 trials of 6 expert golf players. The detection of the horizontal position of the golf club was performed using a computer vision technique followed by the estimation algorithm Darwinian Particle Swarm Optimization (DPSO) in order to obtain a kinematical model of each trial. In this paper, the estimated parameters of the models are used as sample and training data of five classification algorithms: (1) Linear Discriminant Analysis (LDA); (2) Quadratic Discriminant Analysis (QDA); (3) Naïve Bayes with Normal (Gaussian) distribution (NV); (4) Naïve Bayes with Kernel Smoothing Density Estimate (NVK) and (5) Least Squares Support Vector Machines with Radial Basis Function Kernel (LS-SVM). The five classification methods are then compared through the analysis of the confusion matrix and the area under the Receiver Operating Characteristic curve (AUC).
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Acknowledgments
This work was supported by PhD scholarships (SFRH/BD /73382/2010) and (SFRH/BD/64426/2009) by the Portuguese Foundation for Science and Technology (FCT), the Institute of Systems and Robotics (ISR) and RoboCorp at the Engineering Institute of Coimbra (ISEC) also under regular funding by FCT.
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Luz, J.M.A., Couceiro, M.S., Portugal, D., Rocha, R.P., Araújo, H., Dias, G. (2013). Comparison of Classification Methods for Golf Putting Performance Analysis. In: Madureira, A., Reis, C., Marques, V. (eds) Computational Intelligence and Decision Making. Intelligent Systems, Control and Automation: Science and Engineering, vol 61. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4722-7_4
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DOI: https://doi.org/10.1007/978-94-007-4722-7_4
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