Multi‐label feature selection eliminates irrelevant and redundant features, and then improves the performance of multi‐label classification models. Most multi‐label feature selection algorithms assume that the training set contains logical labels, which means that labels are equally important for instances. However, in practical applications, there are different importances with respect to labels. To solve the problem, a multi‐label feature selection method based on relative entropy and fuzzy neighborhood mutual discriminant index is proposed. Firstly, logical labels are converted to label distribution through label enhancement. Secondly, the neighborhood and relative entropy are introduced into the label distribution, the label neighborhood similarity matrix is constructed to describe the similarity of samples under label space. Finally, the fuzzy neighborhood mutual discrimination index is used to combine the candidate features with the label neighborhood similarity matrix, which is used to judge the distinguishing ability of the candidate features. Comprehensive experiment of eight multi‐label datasets shows that the proposed algorithm has better classification performance than other compared algorithms.
In hierarchical classification learning, the feature space of data has high dimensionality and is unknown with emergent features. To solve the above problems, we propose an online hierarchical feature selection algorithm based on adaptive ReliefF. Firstly, ReliefF is adaptively improved via using the density information of instances around the target sample, making it unnecessary to prespecify parameters. Secondly, the hierarchical relationship between classes is used, and a new method for calculating the feature weight of hierarchical data is defined. Then, an online correlation analysis method based on feature interaction is designed. Finally, the adaptive ReliefF algorithm is improved based on feature redundancy, and the feature weight is scaled by the correlation between features in order to achieve the dynamic updating of feature redundancy. A large number of experiments verify the effectiveness of the proposed algorithm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.