Feature selection mechanism based on binary queue search for unbalanced data

The unbalanced data (non-uniform distribution of classes) and the redundant features dramatically increased the difficulty of data accurate classification. Taking the prediction accuracy of the optimal learning algorithm as the goal, combined with the synthetic minority oversampling technology SMOTE, a wrapper feature selection algorithm BQSA was desigend for binary queue search method of unbalanced data. Using 14 kinds of software fault prediction in PROMISE knowledge base to conduct experimental analysis of datasets. The influence of the over-sampling ratio of the dataset is tested, and it is proved that the synthesis of a few over-sampling has a positive effect on the classification prediction of highly unbalanced data, and the optimal over-sampling rate is obtained. The performance of BQSA is compared with similar algorithms, and it is proved that the BQSA algorithm combined with synthetic minority oversampling has better prediction accuracy and better performance in classification sensitivity, specificity and AUC of area under the curve.