Abstract:

For the issue of classification in imbalanced datasets, this paper presents a new differentiated sampling rate algorithm (DSRA), on this basis, a SVM-Ripper ensemble classifier (SREC) is proposed. SREC employs an unique classifier selection strategy, a novel classifier integration approach and an original classification decision-making method, so that it receives a higher classification accuracy. At the same time, the source of classification in an imbalanced dataset is studied by use of SREC. The simulation results prove that the source of classification in an imbalanced dataset is the aggregation of imbalance between classes, imbalance within a class, sample size as well as the imbalance degree, and only a comprehensive consideration of these factors can better address the issue of classification in imbalanced dataset.