Aggregation of Tennis Multivariate Time-Series Using the Choquet Integral and Its Generalizations
Fragment książki (Materiały konferencyjne)
MNiSW
70
konferencja
| Status: | |
| Autorzy: | Skublewska-Paszkowska Maria, Karczmarek Paweł, Powroźnik Paweł, Łukasik Edyta, Smołka Jakub, Dolecki Michał |
| Dyscypliny: | |
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| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Strony: | 1 - 6 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus | IEEE Xplore |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | 2023 IEEE International Conference on Fuzzy Systems |
| Skrócona nazwa konferencji: | FUZZ - IEEE 2023 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 13 sierpnia 2023 do 17 sierpnia 2023 |
| Miasto konferencji: | Incheon |
| Państwo konferencji: | KOREA POŁUDNIOWA |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| 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%. |
