Double Cost Sensitive Random Forest Algorithm

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 s廿ong features more tendentiously without destroying the randomness of random forest; in the integration stage, the misclassification price is introduced to select the decision l:J·ee 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%.

Abstract licence: CC BY

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