%0 Journal Article %A LI Longzhu %A LIN Yaojin %A LYU Yan %A LU Shun %A WANG Chenxi %T Online Streaming Feature Selection Algorithm Using Neighborhood Information Interaction %D 2021 %R 10.3778/j.issn.1002-8331.2010-0096 %J Computer Engineering and Applications %P 102-108 %V 57 %N 21 %X

In the open dynamic environment, the task of machine learning faces the high dimensionality and dynamicity of feature space. At present, the existing online streaming feature selection algorithms generally consider the importance of feature and the redundancy between features, and ignore the interaction between features. Feature interaction denotes a feature irrelevant or weakly relevant with the labels by itself, but when it is combined with some other features, it will be strongly correlated with the labels. Based on this, an online streaming feature selection algorithm based on neighborhood information interaction is proposed, which includes online interaction feature selection and online redundant feature deletion, i.e., calculating the interaction strength between the new arrived feature and the selected feature subset, and deleting redundant features using pair-wise comparison mechanism. Finally, extensive experiments are conducted on ten data sets, and the results show the proposed algorithm is effective.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2010-0096