Activity recognition via correlation coefficients based graph with nodes updated by multi-aggregator approach

Hu, Lingyue; Zhao, Kailong; Ling, Bingo Wing‐Kuen; Lin, Yiguang

doi:10.1016/j.bspc.2022.104255

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…The efficacy of this approach was demonstrated on real-world datasets for traffic flow and groundwater level prediction, resulting in improved performance. Similarly, in the field of activity recognition, Hu et al [36] proposed a correlation coefficient-based method to generate a graph from motion signals, followed by random forest classification, resulting in a significantly high accuracy. Table 2 compares previous studies' use of GNN to predict game outcomes, including the datasets, amount of data and features, the most successful models, and success rates.…”

Section: Gnn Methodology and Sport Outcome Predictionmentioning

confidence: 99%

Enhancing Basketball Game Outcome Prediction through Fused Graph Convolutional Networks and Random Forest Algorithm

Zhao

Chun-jie

Guang-xin

2023

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Basketball is a popular sport worldwide, and many researchers have utilized various machine learning models to predict the outcome of basketball games. However, prior research has primarily focused on traditional machine learning models. Furthermore, models that rely on vector inputs tend to ignore the intricate interactions between teams and the spatial structure of the league. Therefore, this study aimed to apply graph neural networks to basketball game outcome prediction, by transforming structured data into unstructured graphs, to represent the interactions between teams in the 2012–2018 NBA season dataset. Initially, the study used a homogeneous network and undirected graph to build a team representation graph. The constructed graph was fed into a graph convolutional network, which yielded an average success rate of 66.90% in predicting the outcome of games. To improve the prediction success rate, feature extraction based on the random forest algorithm was combined with the model. The fused model yielded the best results, and the prediction accuracy was improved to 71.54%. Additionally, the study compared the results of the developed model with previous studies and the baseline model. Our proposed method considers the spatial structure of teams and the interaction between teams, resulting in superior performance in basketball game outcome prediction. The results of this study provide valuable insights for basketball performance prediction research.

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Section: Gnn Methodology and Sport Outcome Predictionmentioning

confidence: 99%