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摘要： 提出一种新的基于模糊聚类的组合分类器算法，该算法利用模糊聚类技术产生训练样本的分布特征，据此为每一个样本赋予一个权值，来确定它们被采样的概率，利用采样样本训练的分类器调整训练集的采样概率，依次生成新的分类器直至达到一定的精度。该组合分类器算法在UCI的多个标准数据集上进行了测试，并与Bagging和AdaBoost算法进行了比较，实验结果表明新的算法具有更好的健壮性和更高的分类精度。
