Comparison of Classification Methods for Golf Putting Performance Analysis

J. M. Luz, M. S. Couceiro, D. Portugal, R. P. Rocha, H. Araújo and G. Dias

In Proc. of Int. Symp. on Computational Intelligence for Engineering Systems (ISCIES'2011), Coimbra, Portugal, Nov. 16-18, 2011.

Abstract

This paper presents a comparative case study on the classification accuracy between 5 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 5 classification methods are then compared through the analysis of the confusion matrix and the area under the Receiver Operating Characteristic curve (AUC)

Index Terms — Golf putting, classification, evaluation.

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BibTeX

@INPROCEEDINGS(Couceiro_et_al_11d,

AUTHOR = "Luz, J. M. and Couceiro, M. S. and Portugal, D. and Rocha, R. P. and Ara{\’u}jo, H. and Dias, G.",

TITLE = "Comparison of Classification Methods for Golf Putting Performance Analysis",

BOOKTITLE = "Proc. of Int. Symp. on Computational Intelligence for Engineering Systems (ISCIES'2011)",

ADDRESS = "Coimbra, Portugal",

YEAR = "2011",

MONTH = "Nov"

)

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Last update: 07/12/2011