Prediction model of disk failure based on adaptive weighted bagging-GBDT algorithm under imbalanced dataset

Aiming at the problem that the classification algorithm based on machine learning is prone to low accuracy of fault prediction due to the serious imbalance between the number of positive and negative samples in the disk dataset, this paper proposes a disk fault prediction model based on adaptive weighted Bagging-GBDT algorithm. Firstly, a hierarchical under-sampling method based on clustering algorithm is proposed to sample healthy disk samples several times to solve the problem that the random undersampling method is easy to discard potentially useful samples. Secondly, each sample after sampling is combined with all the failed disk samples to obtain several subsets. By training these subsets, a number of GBDT sub-classification models with higher prediction accuracy are established. Finally, the weights of each sub-model are adaptively determined through the neighborhood sample label of the test sample, and the final disk failure prediction model is integrated by weighted hard voting. The experimental results on 8 sets of KEEL imbalanced datasets show that the recall of the negative is increased by an average of 9.46% compared with the existing typical imbalanced learning algorithm. At the same time, the advancement of the method in the fault prediction rate is verified on disk public datasets and the disk data of a scheduling system.