For incomplete data with mixed numerical and symbolic attributes, attribute reduction based
on neighborhood multi-granulation rough sets (NMRS) is an important method to improve …

Outliers carry significant information to reflect an anomaly mechanism, so outlier detection
facilitates relevant data mining. In terms of outlier detection, the classical approaches from …

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 …

When tackling high dimensionality in data mining, online feature selection which deals with
features flowing in one by one over time, presents more advantages than traditional feature …

This paper presents a hybrid filter–wrapper feature subset selection algorithm based on
particle swarm optimization (PSO) for support vector machine (SVM) classification. The filter …

Real-world problems are commonly characterized by a high feature dimensionality, which
hinders the modelling and descriptive analysis of the data. However, some of these data …

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 …

Software quality engineering comprises of several quality assurance activities such as
testing, formal verification, inspection, fault tolerance, and software fault prediction. Until …

Multi-label feature selection is an indispensable technology in multi-semantic high-
dimensional data preprocessing, which has been brought into focus in recent years …

The scale of the radius for constructing neighborhood relation has a great effect on the
results of neighborhood rough sets and corresponding measures. A very small radius …