Many existing clustering methods produce clusters with clear and sharp boundaries, which
does not truly reflect the fact that a cluster may not necessarily have a well-defined boundary …

Multi-label feature selection has grabbed intensive attention in many big data applications.
However, traditional multi-label feature selection methods generally ignore a real-world …

As a supervised learning method, classical rough set theory often requires a large amount of
labeled data, in which concept approximation and attribute reduction are two key issues …

In multi-label learning, each sample is related to multiple labels simultaneously, and
attribute space of samples is with high-dimensionality. Therefore, the key issue for attribute …

With the advent of the age of big data, a typical big data set called limited labeled big data
appears. It includes a small amount of labeled data and a large amount of unlabeled data …

This paper presents a feature selection method for Internet of Things (IoT) information
processing, called MIMIC_FS. The maximal information coefficient (MIC), which can capture …

This paper proposes a Cascaded Random Forest (CRF) method, which can improve the
classification performance by means of combining two different enhancements into the …

The outlier relies on its distinctive mechanism and valuable information to play an important
role in expert and intelligent systems, and thus outlier detection has already been …

The neighborhood rough set model only focuses on the consistent samples whose
neighborhoods are completely contained in some decision classes, and ignores the …

Credit scoring is a procedure to estimate the risk related with credit products which is
calculated using applicants' credentials and applicants' historical data. However, the data …