With increases in feature dimensions and the emergence of hierarchical class structures,
hierarchical feature selection has become an important data preprocessing step in machine …

Fuzzy rough set method provides an effective approach to data mining and knowledge
discovery from hybrid data including categorical values and numerical values. However, its …

A fuzzy rough set is an important rough set model used for feature selection. It uses the fuzzy
rough dependency as a criterion for feature selection. However, this model can merely …

Feature selection is an important data preprocessing technique, and has been widely
studied in data mining, machine learning, and granular computing. However, very little …

The classification of high-dimensional tasks remains a significant challenge for machine
learning algorithms. Feature selection is considered to be an indispensable preprocessing …

Feature evaluation is an important issue in constructing a feature selection algorithm in
kernelized fuzzy rough sets, which has been proven to be an effective approach to deal with …

In the era of big data, both the size and the number of features, samples, and classes
continue to increase, resulting in high-dimensional classification tasks. One characteristic …

Feature selection is an important data preprocessing approach that continues to be
concerned in data mining. It has been extensively used to construct learning models and …

Feature selection has aroused extensive attention and aims at selecting features that are
highly relevant to classification from raw datasets to improve the performance of a learning …

Aiming at the problem of effectively selecting relevant features from heterogeneous data
without decision, a novel feature selection approach is studied based on fuzzy mutual …