| Title |
Basketball Game Data Visualization and Prediction Model Based on Voting Ensemble Learning |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.5.670 |
| Keywords |
Basketball tournament; Data visualization; Ensemble learning; Random forest algorithm; Clustering algorithm; Voting-TRRF |
| Abstract |
Aiming at the insufficient visualization effect of basketball game data, this paper proposes a mixed prediction model based on voting ensemble learning and improved random forest. Combined with voting ensemble algorithm to process data, the final result of basketball game data is obtained. The experiment showed that the average accuracy of the model reached 0.856, the average absolute error was stable within 0.021-0.041, and the response time of real-time event data visualization was less than 0.8 seconds. The core innovation of the research method is to propose a cluster optimization algorithm to solve the inefficient classification of unlabeled data in traditional random forests. A mixed voting strategy is designed, where hard voting is used for discrete event classification and soft voting is used for continuous index prediction to enhance the model generalization ability of complex event scenarios. |