Abstract
To solve the oversampling problem of multi-class small samples and to improve their classification accuracy, we develop an oversampling method based on classification ranking and weight setting. The designed oversampling algorithm sorts the data within each class of dataset according to the distance from original data to the hyperplane. Furthermore, iterative sampling is performed within the class and inter-class sampling is adopted at the boundaries of adjacent classes according to the sampling weight composed of data density and data sorting. Finally, information assignment is performed on all newly generated sampling data. The training and testing experiments of the algorithm are conducted by using the UCI imbalanced datasets, and the established composite metrics are used to evaluate the performance of the proposed algorithm and other algorithms in comprehensive evaluation method. The results show that the proposed algorithm makes the multi-class imbalanced data balanced in terms of quantity, and the newly generated data maintain the distribution characteristics and information properties of the original samples. Moreover, compared with other algorithms such as SMOTE and SVMOM, the proposed algorithm has reached a higher classification accuracy of about 90%. It is concluded that this algorithm has high practicability and general characteristics for imbalanced multi-class samples.
Highlights
Imbalanced data is one of the important problems to be solved in machine learning and data mining
Studies have shown that in the classification process of imbalanced data, the classification hyperplane boundary is shifted to the side of small samples due to the support of large sample size, and small samples are misclassified leading to low classification accuracy of imbalanced data
Through comparing the CI values of different algorithms, it is found that the classification oversampling method proposed in this paper does not show significant superiority in the composite indicator AUC compared with other algorithms, but the CI value of this algorithm is significantly higher than that of SMOTE, SVMOM and SMO+TLK algorithms, which indicates that this algorithm has good sampling functional capability for imbalanced data
Summary
Imbalanced data is one of the important problems to be solved in machine learning and data mining. Imbalance data classification is widely used in data processing in the fields of social surveys, disaster prediction and disease prevention [1,2,3]. Studies have shown that in the classification process of imbalanced data, the classification hyperplane boundary is shifted to the side of small samples due to the support of large sample size, and small samples are misclassified leading to low classification accuracy of imbalanced data. In multi-class imbalanced data, the classification hyperplane is affected by the difference of data sizes of multi-class samples, which makes its classification accuracy unable to meet the needs of scientific computing. The classification of multi-class imbalanced data has become a key problem in data processing research [4].
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