Predicting fatalities among shark attacks: comparison of classifiers

Lim Mei Shi, Aida Mustapha, Yana Mazwin Mohmad Hassim


This paper presents the comparisons of different classifiers on predicting Shark attack fatalities. In this study, we are comparing two classifiers which are Support vector machines (SVMs) and Bayes Point Machines (BPMs) on Shark attacks dataset. The comparison of the classifiers were based on the accuracy, recall, precision and F1-score as the performance measurement. The results obtained from this study showed that BPMs predicted the fatality of shack attack victim experiment with higher accuracy and precision than the SVMs because BPMs have “average” identifier which can minimize the probabilistic error measure. From this experiment, it is concluded that BPMs are more suitable in predicting fatality of shark attack victim as BPMs is an improvement of SVMs.


Bayes point machines; Data mining; Machine learning; Prediction; Support vector machines

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