[논문]배깅과 부스팅 알고리즘을 이용한 핸드볼 결과 예측 비교

김지응; 박종철; 김태규; 이희화; 안지환

doi:10.15207/jkcs.2021.12.8.279

배깅과 부스팅 알고리즘을 이용한 핸드볼 결과 예측 비교
Comparison of Handball Result Predictions Using Bagging and Boosting Algorithms 원문보기

한국융합학회논문지 = Journal of the Korea Convergence Society, v.12 no.8, 2021년, pp.279 - 286

김지응 (상명대학교 스포츠산업학과) , 박종철 (부경대학교 해양스포츠학과) , 김태규 (부경대학교 해양스포츠학과) , 이희화 (상명대학교 스포츠산업학과) , 안지환 (성균관대학교 스포츠과학과)

초록
AI-Helper

본 연구는 여자핸드볼 경기에서 발생되는 움직임 정보를 바탕으로 앙상블 기법의 배깅과 부스팅 알고리즘의 예측력을 비교하고, 움직임 정보의 활용가능성을 분석하는데 목적이 있다. 연구의 목적을 달성하기 위하여 15번의 연습경기에서 관성센서를 활용해 수집한 움직임 정보를 활용한 경기 결과예측을 랜덤포레스트와 Adaboost 알고리즘을 활용해 비교·분석하였다. 연구결과 첫째, 랜덤포레스트 알고리즘의 예측률은 66.9 ± 0.1%로 나타났으며, Adaboost 알고리즘의 예측률은 65.6 ± 1.6%로 나타났다. 둘째, 랜덤포레스트는 승리 결과는 모두 예측하였고, 패배의 결과는 하나도 예측하지 못하였다. 반면, Adaboost 알고리즘은 승리 예측 91.4%, 패배예측 10.4%라고 나타났다. 셋째, 알고리즘의 적합성 여부에서 랜덤포레스트는 과적합의 오류가 없었지만, Adaboost는 과적합의 오류가 나타났다. 본 연구결과를 바탕으로 스포츠경기를 예측할 때 움직임 정보도 활용 가능성을 확인하였으며, 랜덤포레스트 알고리즘이 보다 우수함을 확인하였다.

Abstract ▼ AI-Helper

The purpose of this study is to compare the predictive power of the Bagging and Boosting algorithm of ensemble method based on the motion information that occurs in woman handball matches and to analyze the availability of motion information. To this end, this study analyzed the predictive power of the result of 15 practice matches based on inertial motion by analyzing the predictive power of Random Forest and Adaboost algorithms. The results of the study are as follows. First, the prediction rate of the Random Forest algorithm was 66.9 ± 0.1%, and the prediction rate of the Adaboost algorithm was 65.6 ± 1.6%. Second, Random Forest predicted all of the winning results, but none of the losing results. On the other hand, the Adaboost algorithm shows 91.4% prediction of winning and 10.4% prediction of losing. Third, in the verification of the suitability of the algorithm, the Random Forest had no overfitting error, but Adaboost showed an overfitting error. Based on the results of this study, the availability of motion information is high when predicting sports events, and it was confirmed that the Random Forest algorithm was superior to the Adaboost algorithm.

주제어

표/그림 (14)

표 Table 1. Subject Information
그림 Fig. 1. IMU Sensor Setting
표 Table 2. Factors classified as multicollinearity
표 Table 3. hyper parameter tuning
표 Table 4. hyperparameter value with best prediction
표 Table 5. Confusion matrix
표 Table 6. Random Forest Prediction Results
표 Table 7. Random Forest Confusion Matrix
그림 Fig. 2. Random Forest algorithm conformity
표 Table 8. Random Forest K-fold Cross-validation
표 Table 9. Adaboost Prediction Results
표 Table 10. Adaboost Confusion Matrix
표 Table 11. Adaboost의 K-fold K-fold Cross-validation
그림 Fig. 3. Adaboost algorithm conformity

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