In recent years, it has been difficult for multilabel classification to obtain complete multilabel data in real-world applications, and even a large number of labels for training samples are randomly missed. As a result, the classification task of incomplete multilabel data with missing labels faces formidable challenges. This paper presents a two-stage-neighborhood-based multilabel classification method for incomplete data with missing labels in neighborhood decision systems. First, to solve the problem of selecting the neighborhood radius manually, as well as balancing the samples in the neighborhood, the neighborhood radius based on the feature distribution function is defined, and the differences and similarities between samples through the identifiable and indiscernible matrices are, respectively, computed. Then, a restoration method for missing feature values is proposed for use in the first stage. Second, to consider the nonlinear relationship among features, a neighborhood-based fuzzy similarity relationship between samples is investigated based on the Gaussian kernel function. By integrating the fuzzy similarity relationship matrix, label-specific feature matrix, and label correlation matrix, an objective function based on the regression model is presented, the optimal solutions to the label-specific feature and label correlation matrices based on the gradient descent strategy are provided, and a new multilabel classification method with missing labels is developed during the second stage. Finally, two-stage multilabel classification algorithms are designed. Experiments on 18 multilabel data sets demonstrate that our designed algorithms are effective not only for recovering missing feature values, but also for improving the classification performance of data with missing labels.

The performance of the neighborhood rough set model in feature selection is limited by nonobjective parameter selection method, the uncertainty measures considered only from a single view, and high time cost caused by processing high-dimensional data. To solve the above problems, this study first defines the interclass boundary to granulate the samples in different classes, and three types of neighborhood concepts—negative perspective, neutral perspective, and positive perspective—are put forward based on different cognitive perspectives. Then, the concept of the multiperspective rough set model is developed. The most prominent feature of this model is the discovery of differences between classes from the given data, without any parameters. Second, by integrating the information theory and algebraic views under the multiperspective rough set model, multiview entropy measures are proposed to effectively measure the uncertainty in data. Moreover, a nonmonotonic feature selection algorithm based on the mutual information in the multiview entropy measures under the neutral perspective as the evaluation function of feature importance is designed to resolve the disadvantages of the algorithms based on the monotone evaluation function. Finally, Information Gain is introduced to preliminarily decrease the dimension of high-dimensional data sets to promote classification accuracy and reduce time consumption. The experimental results confirm that the proposed algorithm is efficient in eliminating noise and increasing classification accuracy.