在大数据时代,数据的类别标签数量激增,对现有的分类方法构成了重大挑战。为解决此问题,利用类别间的相似性,将数据类别标签以层次化方式处理。但现有的类别间相似性度量均使用欧氏距离,由于欧氏距离无法有效处理高维数据,因此,受Tanim...在大数据时代,数据的类别标签数量激增,对现有的分类方法构成了重大挑战。为解决此问题,利用类别间的相似性,将数据类别标签以层次化方式处理。但现有的类别间相似性度量均使用欧氏距离,由于欧氏距离无法有效处理高维数据,因此,受Tanimoto系数启发,提出一种新的类别相似性度量方法,使用Louvain算法构建树结构(TaniVT),考虑数据分布,设计基于类内散度的模糊粗糙分层分类器(fuzzy rough hierarchical classifier based on intra-class divergence,IDFRHC),将所提方法与已有的方法进行比较,通过实验验证了所提方法的有效性。展开更多
This paper introduces fuzzy N-bipolar soft(FN-BS)sets,a novel mathematical framework designed to enhance multi-criteria decision-making(MCDM)processes under uncertainty.The study addresses a significant limitation in ...This paper introduces fuzzy N-bipolar soft(FN-BS)sets,a novel mathematical framework designed to enhance multi-criteria decision-making(MCDM)processes under uncertainty.The study addresses a significant limitation in existing models by unifying fuzzy logic,the consideration of bipolarity,and the ability to evaluate attributes on a multinary scale.The specific contributions of the FN-BS framework include:(1)a formal definition and settheoretic foundation,(2)the development of two innovative algorithms for solving decision-making(DM)problems,and(3)a comparative analysis demonstrating its superiority over established models.The proposed framework is applied to a real-world case study on selecting vaccination programs across multiple countries,showcasing consistent DM outcomes and exceptional adaptability to complex and uncertain scenarios.These results position FN-BS sets as a versatile and powerful tool for addressing dynamic DM challenges.展开更多
文摘在大数据时代,数据的类别标签数量激增,对现有的分类方法构成了重大挑战。为解决此问题,利用类别间的相似性,将数据类别标签以层次化方式处理。但现有的类别间相似性度量均使用欧氏距离,由于欧氏距离无法有效处理高维数据,因此,受Tanimoto系数启发,提出一种新的类别相似性度量方法,使用Louvain算法构建树结构(TaniVT),考虑数据分布,设计基于类内散度的模糊粗糙分层分类器(fuzzy rough hierarchical classifier based on intra-class divergence,IDFRHC),将所提方法与已有的方法进行比较,通过实验验证了所提方法的有效性。
文摘This paper introduces fuzzy N-bipolar soft(FN-BS)sets,a novel mathematical framework designed to enhance multi-criteria decision-making(MCDM)processes under uncertainty.The study addresses a significant limitation in existing models by unifying fuzzy logic,the consideration of bipolarity,and the ability to evaluate attributes on a multinary scale.The specific contributions of the FN-BS framework include:(1)a formal definition and settheoretic foundation,(2)the development of two innovative algorithms for solving decision-making(DM)problems,and(3)a comparative analysis demonstrating its superiority over established models.The proposed framework is applied to a real-world case study on selecting vaccination programs across multiple countries,showcasing consistent DM outcomes and exceptional adaptability to complex and uncertain scenarios.These results position FN-BS sets as a versatile and powerful tool for addressing dynamic DM challenges.
