Random Shapley Forests: Cooperative Game-Based Random Forests With Consistency
Authors: Sun, J., Yu, H., Zhong, G., Dong, J., Zhang, S. and Yu, H.
Journal: IEEE Transactions on Cybernetics
Volume: 52
Issue: 1
Pages: 205-214
eISSN: 2168-2275
ISSN: 2168-2267
DOI: 10.1109/TCYB.2020.2972956
Abstract:The original random forests (RFs) algorithm has been widely used and has achieved excellent performance for the classification and regression tasks. However, the research on the theory of RFs lags far behind its applications. In this article, to narrow the gap between the applications and the theory of RFs, we propose a new RFs 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 use 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 RFs 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 RFs, the original RFs, and a classic classifier, support vector machines.
https://eprints.bournemouth.ac.uk/33426/
Source: Scopus
Random Shapley Forests: Cooperative Game-Based Random Forests With Consistency.
Authors: Sun, J., Yu, H., Zhong, G., Dong, J., Zhang, S. and Yu, H.
Journal: IEEE Trans Cybern
Volume: 52
Issue: 1
Pages: 205-214
eISSN: 2168-2275
DOI: 10.1109/TCYB.2020.2972956
Abstract:The original random forests (RFs) algorithm has been widely used and has achieved excellent performance for the classification and regression tasks. However, the research on the theory of RFs lags far behind its applications. In this article, to narrow the gap between the applications and the theory of RFs, we propose a new RFs 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 use 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 RFs 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 RFs, the original RFs, and a classic classifier, support vector machines.
https://eprints.bournemouth.ac.uk/33426/
Source: PubMed
Random Shapley Forests: Cooperative Game-Based Random Forests With Consistency
Authors: Sun, J., Yu, H., Zhong, G., Dong, J., Zhang, S. and Yu, H.
Journal: IEEE TRANSACTIONS ON CYBERNETICS
Volume: 52
Issue: 1
Pages: 205-214
eISSN: 2168-2275
ISSN: 2168-2267
DOI: 10.1109/TCYB.2020.2972956
https://eprints.bournemouth.ac.uk/33426/
Source: Web of Science (Lite)
Random Shapley Forests: Cooperative Game Based Random Forests with Consistency
Authors: Sun, J., Hui, Y., Guoqiang, Z., Junyu, D., Shu, Z. and Hongchuan, Y.
Journal: IEEE Transactions on Cybernetics
https://eprints.bournemouth.ac.uk/33426/
Source: Manual
Random Shapley Forests: Cooperative Game-Based Random Forests With Consistency.
Authors: Sun, J., Yu, H., Zhong, G., Dong, J., Zhang, S. and Yu, H.
Journal: IEEE transactions on cybernetics
Volume: 52
Issue: 1
Pages: 205-214
eISSN: 2168-2275
ISSN: 2168-2267
DOI: 10.1109/tcyb.2020.2972956
Abstract:The original random forests (RFs) algorithm has been widely used and has achieved excellent performance for the classification and regression tasks. However, the research on the theory of RFs lags far behind its applications. In this article, to narrow the gap between the applications and the theory of RFs, we propose a new RFs 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 use 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 RFs 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 RFs, the original RFs, and a classic classifier, support vector machines.
https://eprints.bournemouth.ac.uk/33426/
Source: Europe PubMed Central
Random Shapley Forests: Cooperative Game Based Random Forests with Consistency
Authors: Sun, J., Hui, Y., Guoqiang, Z., Junyu, D., Shu, Z. and Hongchuan, Y.
Journal: IEEE Transactions on Cybernetics
Volume: 52
Issue: 1
Pages: 205-214
ISSN: 2168-2267
Abstract: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.
https://eprints.bournemouth.ac.uk/33426/
Source: BURO EPrints