An oversampling method that fuses boundary factor and data density
Zhemei Zhao, Zhonglin Zhang, Guanghui Yan, Zhihan Liu · 2021 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST) · 2021
Whereas standard classifiers fail to achieve substantial results in dealing with under-represented instances. In this paper, a method of improving SMOTE, BDSMOTE(Fusion of boundary factors and data density for improved SMOTE) is proposed. In this method, the concepts of data density and boundary factor are introduced. Firstly, minority samples are divided into boundary samples and safety samples based on data density, and each minority sample is given initial weight, the safety sample region is combined with more samples, and the boundary sample region is relatively less. Secondly, the boundary factor is introduced to update the sample weight, so that the number of synthetic instances of each sample is different. Then compose a new instance with the SMOTE method. Finally, six pretreatment methods are compared on random forest classifier. Experiments on KEEL and UCI data sets show that the classification model using the proposed method and random forest has better classification performance than the other five classification models.