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 …

Curse of dimensionality is known as big challenges in data mining, pattern recognition,
computer vison and machine learning in recent years. Feature selection and feature …

In many real-world multi-label applications, the content of multi-label data is usually
characterized by high dimensional features, which contains complex correlation information …

Wastewater characteristics prediction in wastewater treatment plants (WWTPs) is valuable
and can reduce the number of sampling, energy, and cost. Feature Selection (FS) methods …

In many real-world application domains, eg, text categorization and image annotation,
objects naturally belong to more than one class label, giving rise to the multi-label learning …

Feature selection attempts to capture the more discriminative features and plays a significant
role in multilabel learning. As an efficient mathematical tool to handle incomplete and …

In this paper, for the first time, a feature selection procedure is modeled as a multi-criteria
decision making (MCDM) process. This method is applied to a multi-label data and we have …

Multi-label feature selection captures a reliable and informative feature subset from high-
dimensional multi-label data, which plays an important role in pattern recognition. In …

Recent years has witnessed urgent needs for addressing the curse of dimensionality
regarding multi-label data, which attracts wide attention for feature selection. Feature …

In recent years, multi-label learning becomes a trending topic in machine learning and data
mining. This type of learning deals with data that each instance is associated with more than …