A Hybrid Sampling Method Based on Safe Screening for Imbalanced Datasets with Sparse Structure
Hongbo Shi, Qigang Gao, Suqin Ji, Yanxin Liu · 2018
Learning from imbalanced datasets is challenging due to the existence of imbalanced class distribution and other problems such as class overlapping, high dimensionality of the dataset and small sample size. This paper focuses on handling the class imbalanced problem with sparsity. A hybrid sampling method of “Under-sampling + Over-sampling” is proposed. Under-sampling selects informative instances and features from the original dataset, whereas Over-sampling balances the under-sampled dataset. Considering that safe double screening can quickly identify and remove non-informative instances and features from the dataset with sparse structure, it is adopted as Under-sampling in the hybrid sampling method. Under-sampling based on safe double screening can obtain all of the boundary instances and the informative features for classification by utilizing sparse structure of datasets. Experimental results show that safe double screening can reduce the number of instances and features and capture the true imbalance ratio of a dataset. The classifiers built on the sampled datasets by using the hybrid sampling method obtain better classification performance, especially for the data in the minority class. This hybrid sampling method is suitable for constructing a classifier based on decision boundary on sparse imbalanced dataset in which the number of features is large.