arXiv · Machine Learning Theory· Maikel Leyva-Vazquez, Dayron Rumbaut Rangel, Lorenzo Cevallos-Torres, Alexis Matheu Perez·· 4 hr agoAI score26
面向不确定性感知轴承故障检测的模糊集成分类：实验室与变速工业基准证据
Neutrosophic Ensemble Classification for Uncertainty-Aware Bearing Fault Detection: Evidence from Laboratory and Variable-Speed Industrial Benchmarks
AI brief
研究将 Random Forest + XGBoost + Logistic Regression 集成分解为 T-hat、F-hat、预测熵 I1-hat 与决策分歧 I2-hat 四个指标，在 CWRU 与 JNU（600-1000 rpm）基准上按 leave-one-condition-out 协议评估。
Source: arXiv · Machine Learning Theory · arxiv.org