Feature selection is a popular technique of preprocessing data. In order to deal with
dynamic or large data, incremental feature selection has been developed, in which the …

In the research spectrum of rough set, the task of attribute reduction is obtaining a minimal
attribute subset that preserves certain properties of the original data. Cost-sensitive attribute …

Fuzzy rough-set-based feature selection is an important technique for big data analysis.
However, the classic fuzzy rough set algorithm takes all the data correlations into account …

The set of permissions required by any Android app during installation time is considered as
the feature set which are used in permission based detection of Android malwares. Those …

Attribute reduction from decision tables has been much focused in recent years in which the
incremental methods of the tradition rough set and extended models are mostly used for …

Local rough sets as a generalization of classical rough sets not only inherit the advantages
of classical rough sets which can handle imprecise, fuzzy and uncertain data, but also break …

Attribute reduction, also known as feature selection, is a common problem by selecting a
subset of relevant attributes (eg features) to reach efficient learning/mining. Many attribute …

This book is based on my Ph. D. dissertation completed at Ghent University (Belgium) and
the University of Granada (Spain) in June 2018. It focuses on classification. The goal is to …

For those key data in feature genes selection which the neighborhood of a sample is not
completely contained in its decision equivalence class, most of existing models lack of …

Highlights•Hyperspectral imaging technology was used for classifying soybean
varieties.•Band selection based on information measure in fuzzy rough set theory was …