关键词: 不平衡数据集, 重采样策略, 分类模型, 评价指标

Abstract:

Due to the inherent characteristics of the imbalanced data set, the classification results are often affected by a large number of categories, resulting in a decline in classification performance. In recent years, a series of research strategies based on improving the accuracy of machine learning classification algorithms for imbalanced data sets have been proposed in order to be able to learn the inherent laws of data from the imbalanced data sets and to tap their potential value. These strategies are mainly based on the data level and the classification model improvement level to solve the difficulty of unbalanced data set classification. From the above two aspects, the machine learning classification strategy for the imbalanced data set classification problem is discussed, the evaluation indicators for the imbalanced data set machine learning classifier are analyzed and discussed, and the existing problems in the imbalanced data set classification are summarized. Finally, looking forward to the direction that can be studied in the future. In particular, the research discusses mainly focuses on the difficulties faced by the binary classification problem in the extreme imbalanced category scenario.

Key words: imbalanced data set, resampling strategy, classification model, evaluation index