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- Title
双重代价敏感随机森林算法.
- Authors
周炎龙; 孙广路
- Abstract
A Double Cost Sensitive Random Forest(DCS-RF) algorithm is proposed to solve the problem that the accuracy of a few classes is not ideal when the classifier identifies unbalanced data. The DCS-RF algorithm introduces the cost sensitive learning in the feature selection stage and the integrated voting stage of the random forest respectively. In the feature selection stage, the method of generating cost vector with lower time complexity is proposed, and the cost vector is introduced into the calculation of split attributes, so that it can select strong features more tendentiously without destroying the randomness of random forest; in the integration stage, the misclassification price is introduced to select the decision tree set which is more sensitive to a few types of data. The experimental results on UCI dataset show that the proposed algorithm has higher overall recognition rate than the comparison method, with an average improvement of 2.46%, and the overall improvement of recognition rate for minority classes is more than 5%.
- Subjects
DECISION trees; RANDOM forest algorithms; PROBLEM solving; FEATURE selection; ALGORITHMS; VOTING; COST
- Publication
Journal of Harbin University of Science & Technology, 2021, Vol 26, Issue 5, p44
- ISSN
1007-2683
- Publication type
Article
- DOI
10.15938/j.jhust.2021.05.006