【摘要】 提出一种新的基于包装方法和过滤器方法的混合特征选择算法RRK。该算法主要包括两个方面:①在特征预选阶段,提出一种基于ReliefF算法的特征加权算法NReliefF和降维算法DR,以实现特征加权并去掉不太重要的特征;②在特征选择阶段,把NReliefF算法和KNN算法结合起来,将预选特征作为输入,用分类正确率来选择最优特征。分别采用一个实际的乘员类型数据集和UCI上的imagine标准数据集进行了实验。实验结果表明,采用RRK后分类的效率和正确率都有明显提高。更多还原

【Abstract】 Feature selection is one of the most important issues in system modeling,data mining and pattern recognition.A new hybrid feature selection method,named RRK,is proposed which is based on the wrapper and filter methods.This method includes two steps:(1) feature pre-selection phase,in this step an improved algorithm named NReliefF based on ReliefF and a dimension reduction algorithm named DR are given to finish feature-weight and useless feature reduction;(2) feature selection phase,in this step the result of the feature pre-selection is used as the input,and the NReliefF and KNN algorithms are combined to select the optimal feature which is based on the highest accuracy rate.Both practical data sets of occupant type and standard data sets of Imagine in UCI are used to verify the proposed feature selection method.Results show that both the accuracy rate and efficiency of the classification are significantly improved by this method.更多还原