After comprehending and analyzing the various classifiers and integration of multi-classifiers, a new method of multi-classifier ensemble is presented in this paper.
本文在理解和分析各种分类器以及分类器集成方法的基础上提出了一种新的多分类器集成的方法。
Furthermore, compared with the general Bayesian classifier ensemble, PEBNC requires less space because there is no need to save parameters of individual classifiers.
此外，与一般的贝叶斯集成分类器相比，PEBNC不必存储成员分类器的参数，空间复杂度大大降低。
For this object, a method of determining fuzzy integral density with membership matrix is proposed, and the classifier ensemble algorithm based on fuzzy integral is introduced.
给出了基于隶属度矩阵的模糊积分密度确定方法，介绍了基于模糊积分的分类器集成算法。
For improving the performance of multiple classifier system, a novel method of ensemble feature selection is proposed based on generalized rough set.
为改善多分类器系统的分类性能，提出了基于广义粗集的集成特征选择方法。
For improving the performance of multiple classifier system, a novel method of ensemble feature selection is proposed based on generalized rough set.
为改善多分类器系统的分类性能，提出了基于广义粗集的集成特征选择方法。
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