Detection of Ionospheric Scintillation Based on XGBoost Model Improved by SMOTE-ENN Technique
نویسندگان
چکیده
Ionospheric scintillation frequently occurs in equatorial, auroral and polar regions, posing a threat to the performance of global navigation satellite system (GNSS). Thus, detection ionospheric is great significance regard improving GNSS performance, especially when severe occurs. Normal algorithms exhibit insensitivity strong that natural phenomenon appears only occasionally, such samples account for small proportion data datasets relative those weak/moderate events. Aiming at accuracy, we proposed strategy combining an improved eXtreme Gradient Boosting (XGBoost) algorithm by using synthetic minority, oversampling technique edited nearest neighbor (SMOTE-ENN) resampling detecting events imbalanced with respect weak, medium scintillation. It outperformed decision tree random forest 12% training validation data, depths ranging from 1 30. For different degrees imbalance datasets, testing accuracy XGBoost was about 4% 5% higher than forest. Meanwhile, results method showed significant increases evaluation indicators, while recall value relatively stable, above 90%, corresponding F1 scores were over 92%. When on imbalance, there distinct increase 10% 20% 6% 11% score events, 90.42% 96.04%.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2021
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs13132577