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- W2945686735 abstract "Basketball outcomes prediction is a vital technique for prospective player arrangement, injury avoidance, telecast right pricing, etc., which requires a understanding of the skill, luck, and other exterior factors of both teams. This paper presents a hybrid ensemble learning framework for basketball outcomes prediction by learning the recent status of the teams. To achieve this, we first design a new weighted combination feature for a future game by considering the latest status of the home team and the visiting team. Then, we present a hybrid ensemble framework equipped with bagging strategy and random subspace method to enlarge the diversity of the samples by learning a series of support vector machines. Finally, we develop a voting mechanism to predict the basketball outcomes. Extensive experiments have demonstrated the outperformance of our framework." @default.
- W2945686735 created "2019-05-29" @default.
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- W2945686735 date "2019-08-01" @default.
- W2945686735 modified "2023-10-15" @default.
- W2945686735 title "A hybrid ensemble learning framework for basketball outcomes prediction" @default.
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- W2945686735 doi "https://doi.org/10.1016/j.physa.2019.121461" @default.
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