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Multi-label learning (MLL) suffers from the high-dimensional feature space teeming with irrelevant and redundant features. To tackle this, several multi-label feature selection (MLFS) algorithms have emerged as vital preprocessing steps. ...

Trackers based on Siamese network have achieved positive performance in recent days. However, most of the existing siamese single object trackers only consider the spatial information in the template which was given in the first frame ...

Multi-label classification is a challenging issue in the data science community due to the ambiguity of label semantics. Existing studies mainly focus on improving label association with logical labels, but the performance suffers from ...

Relative dependency degree is mined by lattice bases and accuracy mechanisms.Relative dependency degree guides approximate reducts: positive quantitative reducts.Three-way quantitative and qualitative reducts are proposed by the relative ...

Stream computing paradigm, with the characteristics of real-time arrival and departure, has been admitted as a major computing paradigm in big data. Relevant theories are flourishing recently with the surge development of stream computing platforms such ...

Neighborhood Covering Reduction extracts rules for classification through formulating the covering of data space with neighborhoods. The covering of neighborhoods is constructed based on distance measure and strictly constrained to be homogeneous. ...

Due to their unsupervised learning nature, analyzing the semantics of clustering schemes can be difficult. Qualitative information such as preference relations may be useful in semantic analysis of clustering process. This paper describes a framework ...

Automatic image annotation is concerned with the task of assigning one or more semantic concepts to a given image. It is a typical multi-label classification problem. This paper presents a novel multi-label classification framework MLNRS based on ...

The precision and grade of the approximate space are two fundamental quantitative indexes that measure the relative and absolute quantitative information, respectively. The double quantification of the precision and grade is a relatively new subject, ...

Bayesian rough set model (BRSM), as the hybrid development between rough set theory and Bayesian reasoning, can deal with many practical problems which could not be effectively handled by original rough set model. In this paper, the equivalence between ...

Rough set theory is an effective supervised learning model for labeled data. However, it is often the case that practical problems involve both labeled and unlabeled data, which is outside the realm of traditional rough set theory. In this paper, the ...

Attribute reduction is one of the key issues in rough set theory. Many heuristic attribute reduction algorithms such as positive-region reduction, information entropy reduction and discernibility matrix reduction have been proposed. However, these ...