计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 270-274.doi: 10.11896/jsjkx.200700036
戴宗明, 胡凯, 谢捷, 郭亚
DAI Zong-ming, HU Kai, XIE Jie, GUO Ya
摘要： 为提高传统机器学习算法的分类精度和泛化能力,提出一种基于直觉模糊集的集成学习算法。根据传统分类器分类精度构建直觉模糊偏好关系矩阵,确定分类器权重,结合多属性群决策方法确定样本分类结果。在UCI中的7个数据集上进行测试,与目前流行的传统分类算法以及集成学习分类算法SVM,LR,NB,Boosting,Bagging相比,提出的算法分类平均精度分别提升了1.91%,3.89%,7.80%,3.66%,4.72%。该算法提高了传统分类方法的分类精度和泛化能力。
中图分类号:
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