In recent years, unsupervised feature selection methods have raised considerable interest in
many research areas; this is mainly due to their ability to identify and select relevant features …

Specialized data preparation techniques, ranging from data cleaning, outlier detection,
missing value imputation, feature selection (FS), amongst others, are procedures required to …

In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale datasets. On the other hand, data mining applications with high …

One of the major problems in Big Data is a large number of features or dimensions, which
causes the issue of “the curse of dimensionality” when applying machine learning …

Feature selection, an optimization problem, becomes an important pre-process tool in data
mining, which simultaneously aims at minimizing feature-size and maximizing model …

Feature Selection (FS) is a challenging machine learning-related task that aims at reducing
the number of features by removing irrelevant, redundant and noisy data while maintaining …

Nowadays, with emerge the multi-label datasets, the multi-label learning processes attracted
interest and increasingly applied to different fields. In such learning processes, unlike single …

In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale medical datasets. On the other, medical applications with high …

The feature selection is an essential data preprocessing stage in data mining. The core
principle of feature selection seems to be to pick a subset of possible features by excluding …

Since different features may require different costs, the cost-sensitive feature selection
problem become more and more important in real-world applications. Generally, it includes …