Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …

Feature selection has been an important issue in machine learning and data mining, and is
unavoidable when confronting with high‐dimensional data. With the advent of multilabel …

In recent years, feature selection for multilabel classification has attracted attention in
machine learning and data mining. However, some feature selection methods ignore the …

Feature selection is a significant task in data mining and machine learning applications
which eliminates irrelevant and redundant features and improves learning performance. In …

This survey aims at providing a state-of-the-art overview of feature selection and fusion
strategies, which select and combine multi-view features effectively to accomplish …

Feature selection, as a dimensionality reduction technique, aims to choosing a small subset
of the relevant features from the original features by removing irrelevant, redundant or noisy …

Purposes To evaluate the capability of PET/CT images for differentiating the histologic
subtypes of non-small cell lung cancer (NSCLC) and to identify the optimal model from …

Recently, multilabel classification has generated considerable research interest. However,
the high dimensionality of multilabel data incurs high costs; moreover, in many real …

Video semantic recognition usually suffers from the curse of dimensionality and the absence
of enough high-quality labeled instances, thus semisupervised feature selection gains …

Feature selection (FS), which identifies the relevant features in a data set to facilitate
subsequent data analysis, is a fundamental problem in machine learning and has been …