山东大学学报 (工学版) ›› 2024, Vol. 54 ›› Issue (4): 59-66.doi: 10.6040/j.issn.1672-3961.0.2023.116
白琳1,2,俱通1,王浩1,雷明珠1,潘晓英1,2
BAI Lin1,2, JU Tong1, WAND Hao1, LEI Mingzhu1, PAN Xiaoying1,2
摘要： 为有效解决欠采样技术在处理不平衡数据时的伪平衡问题,提出并设计一种基于欠采样的提升均衡集成学习算法。采用新的均衡采样机制,通过分箱操作协调数据的预测概率,生成高质量的训练子集,以此迭代训练分类器。基于基分类器在原始数据上的假阳性率和假阴性率,在迭代过程中自适应为其分配权重,避免性能较差的分类器影响整体决策,提高集成模型的泛化能力。新的算法能够在消除伪平衡的同时增加多数类样本的识别度,从而降低边界模糊对分类模型的影响。通过18组小型数据集和2组大型数据集的对比试验表明,该算法具有处理不平衡数据分类问题的优势。
中图分类号:
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