岩性识别类别不平衡ADASYN-SSA-XGBoost高效,精确的岩性识别有助于地质特征描述,提高铀矿勘探开发效率.机器学习技术具有较强的数据挖掘能力,在识别研究中表现出较大的优势.鉴于此,本文以松辽盆地南部某砂岩型铀矿区为研究对象,将所选研究区域内的常规测井数据作为数据集,采用改进后的机器学习方法对常规测井数据进行岩性...
引入了ADASYN自适应采用算法,进行数据集重平衡;(2)通过XGBoost算法搭建了基于气象特征的光伏出力模型,并与传统的BP神经网络进行比较.通过某光伏电站的实际历史数据预测结果比较,结合ADASYN过采样和XGBoost算法,能有效提升模型的准确性;较BP神经网络相比,ADASYN-XGBoost算法的MAE,RMSE,MAPE和R2分别提高了66.7%,68.9%,58.0...
First, an XGBoost-based noise detection model is used to identify data anomalies. Second, adaptive resampling with ADASYN is done to fine-tune the class distribution to address class imbalance. Third, ensemble learning performs intrusion detection through a Random Forest classifier. This stacked model...
SMOTE emerged as the most effective upsampling method, particularly when used with XGBoost, whereas Random Forest performed poorly under severe imbalance. ADASYN showed moderate effectiveness with XGBoost but underperformed with Random Forest, and GNUS produced inconsistent results. This study underscores ...