Abstract：For highly unbalanced data, insufficient learning of minority class samples is caused by self-sampling method of the traditional cost sensitive random forest algorithm, and the cost sensitive mechanism of the algorithm is easily weakened by the large proportion of majority class samples. Therefore, a weak balance cost sensitive random forest algorithm based on clustering is proposed. After clustering the majority class samples, the weak balance criterion is used to reduce the samples of each cluster repeatedly. The selected majority class samples and the minority class samples of the original training set are fused to generate a number of new unbalanced datasets for the training of cost sensitive decision tree. The proposed algorithm not only enables the minority class samples to be fully learned, but also ensures that the cost sensitive mechanism is less affected by reducing the majority class samples. Experiment indicates the better performance of the proposed algorithm in processing highly unbalanced datasets.
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2020, Vol. 33 
