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Random Shapley Forests: Cooperative Game Based Random Forests with Consistency.

Sun, J., Hui, Y., Guoqiang, Z., Junyu, D., Shu, Z. and Hongchuan, Y., 2022. Random Shapley Forests: Cooperative Game Based Random Forests with Consistency. IEEE Transactions on Cybernetics, 52 (1), 205-214.

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DOI: 10.1109/TCYB.2020.2972956


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-known solutions 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.

Item Type:Article
Additional Information:Funding Agency: Major Project for New Generation of AI (Grant Number: 2018AAA0100400) 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 41706010) Science and Technology Program of Qingdao (Grant Number: 17-3-3-20-nsh) CERNET Innovation Project (Grant Number: NGII20170416) Joint Fund of the Equipments Preresearch and Ministry of Education of China (Grant Number: 6141A020337) Open Fund of Engineering Research Center for Medical Data Mining and Application of Fujian Province (Grant Number: MDM2018007) 10.13039/501100012226-Fundamental Research Funds for the Central Universities of China
Uncontrolled Keywords:Random forests; feature evaluation; Shapley; value; consistency
Group:Faculty of Media & Communication
ID Code:33426
Deposited By: Symplectic RT2
Deposited On:21 Feb 2020 13:17
Last Modified:14 Mar 2022 14:20


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