Missing multi-label learning is to address the problem of missing labels in multi-label datasets for multi-label classification tasks. Notably, the complex dependencies that typically exist between labels make accurate classification particularly challenging in the presence of missing labels. Some existing missing multi-label classification models often utilize feature selection to effectively recognize the dependencies between labels and features. However, they are ineffective at capturing hierarchical relationships of feature information, probably leading to a decline in prediction performance. To address this problem, this paper proposes a missing multi-label classification model based on multi-level stochastic concept clustering (MML-MSCC) to make dependencies between features and labels recognized more accurately and prediction performance better. In our model, optimal granularity selection is achieved through the global mutual information between features and labels, which makes the study of stochastic granule concept across multiple granularities. Furthermore, we utilize a stochastic concept clustering method to combine similar feature information for the purpose of making the missing label completion more reasonable. Note that stochastic granule concept clustering is performed with cross-granularity, thereby effectively capturing hierarchical relationships among feature information. Finally, to evaluate the performance of our model, we compare the MML-MSCC model with 9 existing missing multi-label classification models on 12 open datasets in terms of six evaluation metrics.

Concept-cognitive learning is an emerging area of cognitive computing, which refers to continuously learning new knowledge by imitating the human cognition process. However, the existing research on concept-cognitive learning is still at the level of complete cognition as well as cognitive operators, which is far from the real cognition process. Meanwhile, the current classification algorithms based on concept-cognitive learning models (CCLMs) are not mature enough yet since their cognitive results highly depend on the cognition order of attributes. To address the above problems, this article presents a novel concept-cognitive learning method, namely, stochastic incremental incomplete concept-cognitive learning method (SI2CCLM), whose cognition process adopts a stochastic strategy that is independent of the order of attributes. Moreover, a new classification algorithm based on SI2CCLM is developed, and the analysis of the parameters and convergence of the algorithm is made. Finally, we show the cognitive effectiveness of SI2CCLM by comparing it with other concept-cognitive learning methods. In addition, the average accuracy of our model on 24 datasets is 82.02%, which is higher than the compared 20 classification algorithms, and the elapsed time of our model also has advantages.

Granule description is an important problem in knowledge granularity and representation. In the theoretical study of granule description, two basic sub-problems need to be solved: (1) check whether a target granule is definable, and try to offer an exact description for the target granule if definable; (2) an appropriate approximate description should also be provided to the target granule if indefinable. Up to now, there have been some preliminary studies on these two sub-problems. However, the existing findings are not enough or incomplete since sufficient and necessary conditions of definable granules have not been explored, and there have not been effective ways to find better approximate descriptions of indefinable granules. Motivated by these discussions, this paper puts forward granule description methods for definable and indefinable granules. First of all, we propose the notions of covering elements and inserting elements of a target granule by which sufficient and necessary conditions of ∧-definable and (∧,¬)-definable granules are constructed. And then, the smallest definable granule containing the target indefinable granule and the largest definable granule included in the target indefinable granule are investigated, which are used to solve the problem of finding better approximate descriptions for indefinable granules. Finally, a comparison is made to show that the existing work on complete sets should be improved for providing feasible solutions to the granule description problem.

形式概念分析中多粒度形式背景存在两种表示方法:一种是由多粒度标记属性诱导出的多粒度形式背景,另一种是由属性粒化诱导出的多粒度形式背景.为了对这两种表示方法进行对比研究,本文基于布尔属性取值分布提出了类属性块的概念,在此基础上定义可类属性块分割的形式背景,表明了当原始形式背景为可类属性块分割时多粒度形式背景的两种表示方法能够相互转化.可类属性块分割的研究方法为进一步讨论多粒度形式背景奠定了基础.