关键词: 模糊隶属度, 核函数, 组合分类器, 数据集分割

Abstract: The ensemble classifiers train its base classifiers using the datasets which are generated by some rules.This paper presents a fuzzy membership function based on kernel method to divide the training set into two parts，one is easy to classify while another is hard.Two different base classifiers are trained for fitting them；those two kinds of classifiers are integrated as base classifiers.This method is applied to classify the UCI benchmark datasets，and the experimental results show that this method is superior to Bagging and AdaBoost algorithms on the higher performance.

Key words: fuzzy memberships, kernel function, ensemble classifiers, dataset partition