The imbalance of data sets category is one of the important problems in the classification field. The unbalanced index of each data set is closely related to itself, it is a key indicator of data sets. To deal with the classification design of unbalanced data sets, this paper proposed an enhanced AdaBoost(E-AdaBoost) algorithm. In the process of iteration, the algorithm took into account unbalanced index, and the classification accuracy of the minority classed that was more important in unbalanced data sets improving the weight updating strategy of the base classifier, and thus promoting the classification performance of unbalanced data sets. The classification design method of unbalanced data sets based on E-AdaBoost could determine the weight parameters of the base classifier according to the sample unbalanced index, so as to improve the performance of the classifier. With this method that was combined with multiple classical classifiers, this paper carried out experimental analysis in terms of artificial data sets and standard data sets, and compared with relevant methods. The results show that the classification design method of unbalanced data sets based on E-AdaBoost can effectively improve the classification performance of unbalanced data sets.

[2023-07-10] Accepted Paper /doc/2023.04.0163/v1

[2023-12-05] Printed Article /doc/2023.04.0163/v2