# An Undersampling Method Approaching the Ideal Classification Boundary for Imbalance Problems
## 3 Proposed Method
### Undersampling That Enables Close-Fitting Data Distribution at the Ideal Classification Boundary

Table 7: G-mean results using various comparison methods. Abbreviations: Tomek, NB-Tomek; Centers, Centers_nn. ## 5 Conclusions

The imbalanced classification method based on clustering is first used to perform cluster analysis and then perform a random sampling in each cluster to obtain a balanced data set. This traditional approach enables the characteristics of the original data set distribution to be obtained; however, it also retains the data with a low contribution to the classification and thus reduces the accuracy of the classification model. To solve this problem, we propose the FDUS method. It firstly runs the clustering algorithm in the majority class, and then performs a random sampling in the borderline area near to the minority class in the clusters just obtained. In this way, the classification boundary obtained with FDUS conforms to the original shape of the data set between the two classes. In order to enhance the consistency between the classification boundary and the ideal shape, we adopted an ensemble learning method that utilizes multiple classifiers to improve the algorithm. According to the experimental analysis performed on the use of different numbers of classifiers for ensemble learning in FDUS, we put forward the hypothesis that there is an optimal number of models to be in ensemble learning, and proved it via the

Figure 7: Box plots with AUC. performance of a hypothesis test. Having been validated by 20 data sets and compared with seven baseline methods, FDUS conclusively and consistently exhibits superior performance. The FDUS method also, however, exhibits limitations, as it can only be applied to two-class classification problems. Meanwhile, the problem of imbalance exists in different fields, which may require the handling of multimodal data. In the future, we will extend our research to the evaluation of multi-class classification problems and multimodal data. Investigation, C.L. ; Writing--original draft, W.Z. and P.Y. ; Writing--review and editing, C.L. and L.J. All authors have read and agreed to the published version of the manuscript. This research was funded by the Open Fund Project of the National Key Laboratory of Offshore Oil and Gas Development. Not applicable. Data Availability Statement: Data is contained within the article. The authors would like to express their gratitude to [PERSON], a technical expert at the National Key Laboratory of Offshore Oil and Gas Exploration, for providing guidance on this paper. Authors [PERSON] and [PERSON] were employed by the company CNOOC Research Institute Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## References

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