Random shapley forests: cooperative game based random forests with consistency
Research output: Contribution to journal › Article › peer-review
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.
|Number of pages||10|
|Journal||IEEE Transactions on Cybernetics|
|Early online date||23 Mar 2020|
|Publication status||Early online - 23 Mar 2020|
- Random Shapley Forests: Cooperative Game Based Random Forests with Consistency
Rights statement: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript (Post-print), 1.03 MB, PDF document