确定模糊密度是应用模糊积分进行融合的核心问题.分析了目前存在的各类模糊密度赋值方法存在的不足,在探索证据相互关系的基础上,提出了证据支持度的概念,并基于证据支持度给出了一种动态模糊密度赋值方法,分析了分类器之间的差异性对证据支持度的影响,引入了影响因子,进一步完善了证据支持度的内涵.实验表明,本文算法较之单支持向量机、基于投票的支持向量机、以基分类器精度作为模糊密度的静态模糊积分支持向量机集成以及一种基于自适应模糊密度赋值的模糊积分方法用于支持向量机集成,分类精度均得到提升.%The core problem in using fuzzy integral is to determine the fuzzy densities. Through analyzing the disadvantage of the foregone methods of determining fuzzy densities, a new method of determining fuzzy densities adaptively is presented depending upon evidences support degree which is defined based on the interrelation of evidences. The influence of diversity measure among sorters on the evidence support degree is analyzed, and an influencing factor is put forward to perfect the meaning of the evidence support degree. Experimental results indicate that the proposed method has high precision compared with the single support vector machine, voting-based support vector machines, support vector machines static fuzzy integral using the precision of classifiers as fuzzy densities, and fuzzy integral based on fuzzy densities determined adaptively using for support vector machines ensemble.
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