Random Shapley Forests: Cooperative Game-Based Random Forests With Consistency
| Autor: | Jianyuan Sun, Hui Yu, Hongchuan Yu, Guoqiang Zhong, Junyu Dong, Shu Zhang |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Support Vector Machine
Computer science 02 engineering and technology computer.software_genre 01 natural sciences 010104 statistics & probability 0202 electrical engineering electronic engineering information engineering Shapley value 0101 mathematics Electrical and Electronic Engineering consistency Random forests Computer Science Applications Random forest feature evaluation Human-Computer Interaction Support vector machine Control and Systems Engineering 020201 artificial intelligence & image processing Game based Data mining Classifier (UML) computer Algorithms Software Information Systems |
| Zdroj: | Sun, J, Yu, H, Zhong, G, Dong, J, Zhang, S & Yu, H 2020, ' Random shapley forests: cooperative game based random forests with consistency ', IEEE Transactions on Cybernetics . https://doi.org/10.1109/TCYB.2020.2972956 |
| ISSN: | 2168-2275 2168-2267 |
| DOI: | 10.1109/tcyb.2020.2972956 |
| Popis: | The original random forests algorithm has been widely used and has achieved excellent performance for the classification and regression tasks. However, the research on the theory of random forests lags far behind its applications. In this paper, to narrow the gap between the applications and theory of random forests, we propose a new random forests algorithm, called random Shapley forests (RSFs), based on the Shapley value. The Shapley value is one of the well-knownsolutions in the cooperative game, which can fairly assess the power of each player in a game. In the construction of RSFs, RSFs uses the Shapley value to evaluate the importance of each feature at each tree node by computing the dependency among the possible feature coalitions. In particular, inspired by the existing consistency theory, we have proved the consistency of the proposed random forests algorithm. Moreover, to verify the effectiveness of the proposed algorithm, experiments on eight UCI benchmark datasets and four real-world datasets have been conducted. The results show that RSFs perform better than or at least comparable with the existing consistent random forests, the original random forests and a classic classifier, support vector machines. |
| Databáze: | OpenAIRE |
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