机译:优化加权极端学习机,以实现不平衡分类和应用于信用卡欺诈检测
Tongji Univ Dept Comp Sci Shanghai 201804 Peoples R China|Bengbu Univ Sch Comp Engn Bengbu 233030 Anhui Peoples R China;
Tongji Univ Dept Comp Sci Shanghai 201804 Peoples R China;
New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA|King Abdulaziz Univ Ctr Res Excellence Renewable Energy & Power Syst Jeddah 21481 Saudi Arabia;
Tongji Univ Dept Comp Sci Shanghai 201804 Peoples R China;
King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21481 Saudi Arabia|King Abdulaziz Univ Ctr Res Excellence Renewable Energy & Power Syst Jeddah 21481 Saudi Arabia;
Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China;
Imbalanced classification; Weighted Extreme Learning Machine; Dandelion algorithm with probability-based mutation; Credit card fraud detection;
机译:使用机器学习算法从非衡度数据集中的信用卡欺诈检测
机译:一种具有动态加权熵的混合方法,用于处理信用卡欺诈检测中重叠的类别不平衡问题
机译:用于实施分类的差异约束加权极限学习机
机译:使用机器学习技术与数据不平衡解决方案组合的信用卡欺诈检测的比较研究
机译:机器学习技术在信用卡欺诈检测中的应用
机译:缩回:医学数据集分类:结合粒子群优化与极限学习机分类器的机器学习范例
机译:关于信用卡欺诈检测预测准确性的比较研究,不平衡分类