Zgadzam się
Nasza strona zapisuje niewielkie pliki tekstowe, nazywane ciasteczkami (ang. cookies) na Twoim urządzeniu w celu lepszego dostosowania treści oraz dla celów statystycznych. Możesz wyłączyć możliwość ich zapisu, zmieniając ustawienia Twojej przeglądarki. Korzystanie z naszej strony bez zmiany ustawień oznacza zgodę na przechowywanie cookies w Twoim urządzeniu.
The main aim of this paper is to increase the
recognition accuracy of tennis movements obtained by various
crisp classifiers on a basis Choquet integral and its general-
izations serving as aggregation operators. The tennis forehand,
backhand, and volley were taken into consideration with three-
dimensional coordinates obtained from a motion capture system.
The whole silhouette, together with the tennis racket, were
involved in the study. The following classifiers with and without
input fuzzification were applied: Naive Bayes, k-NN, Extreme
Gradient Boosting, Light Gradient Boosting Machine, Random
Forest, Extra Trees, CatBoost, Gradient Boosting, Ada Boost,
Decision Tree, Ridge, and SVM. For the individual classifiers the
highest accuracy of 75.10% was obtained without fuzzification
and of 88.25% with fuzzification. Examinations with 25 families
of t-norms were conducted and the best functions were chosen,
including the Open Newton-Cotes 5 point quadrature. As a result
of the aggregation the classification accuracy was improved and
reached a lever of 90%.
