Classificationand Diagnosisof Mild Cognitive Impairment Basedonthe Mixture Feature Selection
GUO-Hongwei
HU-Bin
Abstract：In order to improve the diagnostic effect of mild cognitive impairment(Mild Cognitive Impairment,MCI), this paper presents a mixture feature selection method Relief-SVMREF algorithm based on Relief and SVMRFE algorithm. Firstly we use the Relief algorithm to remove invalid characters. The relief algorithm does not remove the redundant features, we use the Pearson correlation coefficient to remove the redundant features. Finally, the feature is sorted by SVMRFE algorithm, and the final ranking sequence is obtained. We obtain the optimal subset with the left one cross validation method, and then we use the SVM to do classification. The results show that the method can get better results compared with the two method along.
Keywords：Mild cognitive impairmentSupport Vector Machine Recursive FeatureEliminationReliefmixture feature selectionPearson correlation coefficient
Publication Date：2015-01-01
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：4( 165-168 )