Banzhaf random forests: Cooperative game theory based random forests with consistency
Authors: Sun, J., Zhong, G., Huang, K. and Dong, J.
Journal: Neural Networks
Volume: 106
Pages: 20-29
eISSN: 1879-2782
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2018.06.006
Abstract:Random forests algorithms have been widely used in many classification and regression applications. However, the theory of random forests lags far behind their applications. In this paper, we propose a novel random forests classification algorithm based on cooperative game theory. The Banzhaf power index is employed to evaluate the power of each feature by traversing possible feature coalitions. Hence, we call the proposed algorithm Banzhaf random forests (BRFs). Unlike the previously used information gain ratio, which only measures the power of each feature for classification and pays less attention to the intrinsic structure of the feature variables, the Banzhaf power index can measure the importance of each feature by computing the dependency among the group of features. More importantly, we have proved the consistency of BRFs, which narrows the gap between the theory and applications of random forests. Extensive experiments on several UCI benchmark data sets and three real world applications show that BRFs perform significantly better than existing consistent random forests on classification accuracy, and better than or at least comparable with Breiman's random forests, support vector machines (SVMs) and k-nearest neighbors (KNNs) classifiers.
https://eprints.bournemouth.ac.uk/33294/
Source: Scopus
Banzhaf random forests: Cooperative game theory based random forests with consistency.
Authors: Sun, J., Zhong, G., Huang, K. and Dong, J.
Journal: Neural Netw
Volume: 106
Pages: 20-29
eISSN: 1879-2782
DOI: 10.1016/j.neunet.2018.06.006
Abstract:Random forests algorithms have been widely used in many classification and regression applications. However, the theory of random forests lags far behind their applications. In this paper, we propose a novel random forests classification algorithm based on cooperative game theory. The Banzhaf power index is employed to evaluate the power of each feature by traversing possible feature coalitions. Hence, we call the proposed algorithm Banzhaf random forests (BRFs). Unlike the previously used information gain ratio, which only measures the power of each feature for classification and pays less attention to the intrinsic structure of the feature variables, the Banzhaf power index can measure the importance of each feature by computing the dependency among the group of features. More importantly, we have proved the consistency of BRFs, which narrows the gap between the theory and applications of random forests. Extensive experiments on several UCI benchmark data sets and three real world applications show that BRFs perform significantly better than existing consistent random forests on classification accuracy, and better than or at least comparable with Breiman's random forests, support vector machines (SVMs) and k-nearest neighbors (KNNs) classifiers.
https://eprints.bournemouth.ac.uk/33294/
Source: PubMed
Banzhaf random forests: Cooperative game theory based random forests with consistency
Authors: Sun, J., Zhong, G., Huang, K. and Dong, J.
Journal: NEURAL NETWORKS
Volume: 106
Pages: 20-29
eISSN: 1879-2782
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2018.06.006
https://eprints.bournemouth.ac.uk/33294/
Source: Web of Science (Lite)
Banzhaf random forests: Cooperative game theory based random forests with consistency
Authors: Sun, J., Zhong, G., Huang, K. and Dong, J.
Journal: Neural Networks
Volume: 106
Publisher: Elsevier
ISSN: 0893-6080
Abstract:Random forests algorithms have been widely used in many classification and regression applications. However, the theory of random forests lags far behind their applications. In this paper, we propose a novel random forests classification algorithm based on cooperative game theory. The Banzhaf power index is employed to evaluate the power of each feature by traversing possible feature coalitions. Hence, we call the proposed algorithm Banzhaf random forests (BRFs). Unlike the previously used information gain ratio, which only measures the power of each feature for classification and pays less attention to the intrinsic structure of the feature variables, the Banzhaf power index can measure the importance of each feature by computing the dependency among the group of features. More importantly, we have proved the consistency of BRFs, which narrows the gap between the theory and applications of random forests. Extensive experiments on several UCI benchmark data sets and three real world applications show that BRFs perform significantly better than existing consistent random forests on classification accuracy, and better than or at least comparable with Breiman’s random forests, support vector machines (SVMs) and k-nearest neighbors (KNNs) classifiers.
https://eprints.bournemouth.ac.uk/33294/
Source: Manual
Banzhaf random forests: Cooperative game theory based random forests with consistency.
Authors: Sun, J., Zhong, G., Huang, K. and Dong, J.
Journal: Neural networks : the official journal of the International Neural Network Society
Volume: 106
Pages: 20-29
eISSN: 1879-2782
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2018.06.006
Abstract:Random forests algorithms have been widely used in many classification and regression applications. However, the theory of random forests lags far behind their applications. In this paper, we propose a novel random forests classification algorithm based on cooperative game theory. The Banzhaf power index is employed to evaluate the power of each feature by traversing possible feature coalitions. Hence, we call the proposed algorithm Banzhaf random forests (BRFs). Unlike the previously used information gain ratio, which only measures the power of each feature for classification and pays less attention to the intrinsic structure of the feature variables, the Banzhaf power index can measure the importance of each feature by computing the dependency among the group of features. More importantly, we have proved the consistency of BRFs, which narrows the gap between the theory and applications of random forests. Extensive experiments on several UCI benchmark data sets and three real world applications show that BRFs perform significantly better than existing consistent random forests on classification accuracy, and better than or at least comparable with Breiman's random forests, support vector machines (SVMs) and k-nearest neighbors (KNNs) classifiers.
https://eprints.bournemouth.ac.uk/33294/
Source: Europe PubMed Central
Banzhaf random forests: Cooperative game theory based random forests with consistency.
Authors: Sun, J., Zhong, G., Huang, K. and Dong, J.
Journal: Neural Networks
Volume: 106
Pages: 20-29
ISSN: 0893-6080
Abstract:Random forests algorithms have been widely used in many classification and regression applications. However, the theory of random forests lags far behind their applications. In this paper, we propose a novel random forests classification algorithm based on cooperative game theory. The Banzhaf power index is employed to evaluate the power of each feature by traversing possible feature coalitions. Hence, we call the proposed algorithm Banzhaf random forests (BRFs). Unlike the previously used information gain ratio, which only measures the power of each feature for classification and pays less attention to the intrinsic structure of the feature variables, the Banzhaf power index can measure the importance of each feature by computing the dependency among the group of features. More importantly, we have proved the consistency of BRFs, which narrows the gap between the theory and applications of random forests. Extensive experiments on several UCI benchmark data sets and three real world applications show that BRFs perform significantly better than existing consistent random forests on classification accuracy, and better than or at least comparable with Breiman’s random forests, support vector machines (SVMs) and k-nearest neighbors (KNNs) classifiers.
https://eprints.bournemouth.ac.uk/33294/
Source: BURO EPrints