基于多模型组合的类别不平衡海洋数据质量控制方法OA北大核心CSTPCD

This paper proposes a two-layer framework for ocean data quality control based on the combination of multiple models.Various common classification algorithms are chosen as base learners to predict the primary quality labels of ocean data,and a Voting or Stacking strategy is used to identify the quality of the data.To address the issue of class imbalance,an adaptive undersampling strategy is combined with the Focal loss function to enhance the model's ability to recognize difficult samples.To verify the performance of the proposed method,we apply it to the quality control of sea surface temperature and air temperature data that are from ICOADS(International Comprehensive Ocean-Atmosphere Data Set).The results show that the F1 score(the weighted harmonic mean of precision and recall)of rare anomaly samples by the Voting or Stacking methods can reach 0.980 6 and 0.981 2 for sea surface temperature data,and 0.998 5 and 0.998 3 for air temperature data.