Feature selection via multi-view perturbation in a type of weakly supervised data
Technique of disambiguation of weak labels can be used to remove noisy labels for samples from data.However,redundant or irrelevant features may also be observed after disambiguation of weak labels,so the prob-lem of feature selection should be paid much attention to in weakly supervised data.On the basis of the data with disambiguation of weak labels,a general feature selection framework via multi-view perturbation is developed,which can construct different perturbed data from both the levels of sample and feature.Consequently,multiple results of feature selection can be obtained,which provide a basic integration tool for the subsequent learning.The proposed framework can be applied to various forms and constraints of searching.On more than 12 sets of high-dimensional data,by injecting 5 ratios of label noise and using 3 criteria of feature evaluation,the experi-mental results demonstrate that the feature selection results obtained by our proposed method can significantly im-prove the classification performance from both the aspects of classification accuracy and classification stability.
万方数据
维普
