Abstract:

We study the class-imbalanced problem of software defect prediction and propose an integrated sampling method  for class-imbalanced data classification so as to enhance the classification ability.In order to avoid the blindness of random sampling,we utilize the integrated sampling method to balance datasets:using SMOTE for over-sampling minority class and KMeans clustering for down-sampling majority class.After obtaining a balanced dataset,we utilize multiple single classifiers to ensemble learning. Experimental results show that the software defect prediction algorithm,which combines integrated sampling and ensemble learning,has better classification performance,obtaining a higher true positive rate while significantly reducing the false alarm rate. 

Key words: unbalanced dataset;SMOTE;K-Means;vote;ensemble learning