Fuzzy rough sets and fuzzy rough neural networks for feature selection: A review

W Ji, Y Pang, X Jia, Z Wang, F Hou… - … : Data Mining and …, 2021‏ - Wiley Online Library
Feature selection aims to select a feature subset from an original feature set based on a
certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it …

Machine learning algorithms for network intrusion detection

J Li, Y Qu, F Chao, HPH Shum, ESL Ho, L Yang - AI in Cybersecurity, 2019‏ - Springer
Network intrusion is a growing threat with potentially severe impacts, which can be
damaging in multiple ways to network infrastructures and digital/intellectual assets in the …

Feature selection based on neighborhood discrimination index

C Wang, Q Hu, X Wang, D Chen… - IEEE transactions on …, 2017‏ - ieeexplore.ieee.org
Feature selection is viewed as an important preprocessing step for pattern recognition,
machine learning, and data mining. Neighborhood is one of the most important concepts in …

GBRS: A unified granular-ball learning model of pawlak rough set and neighborhood rough set

S **a, C Wang, G Wang, X Gao, W Ding… - … on Neural Networks …, 2023‏ - ieeexplore.ieee.org
Pawlak rough set (PRS) and neighborhood rough set (NRS) are the two most common
rough set theoretical models. Although the PRS can use equivalence classes to represent …

A group incremental approach to feature selection applying rough set technique

J Liang, F Wang, C Dang, Y Qian - IEEE transactions on …, 2012‏ - ieeexplore.ieee.org
Many real data increase dynamically in size. This phenomenon occurs in several fields
including economics, population studies, and medical research. As an effective and efficient …

Ensemble feature selection using bi-objective genetic algorithm

AK Das, S Das, A Ghosh - Knowledge-Based Systems, 2017‏ - Elsevier
Feature selection problem in data mining is addressed here by proposing a bi-objective
genetic algorithm based feature selection method. Boundary region analysis of rough set …

Local neighborhood rough set

Q Wang, Y Qian, X Liang, Q Guo, J Liang - Knowledge-Based Systems, 2018‏ - Elsevier
With the advent of the age of big data, a typical big data set called limited labeled big data
appears. It includes a small amount of labeled data and a large amount of unlabeled data …

Incremental learning based on granular ball rough sets for classification in dynamic mixed-type decision system

Q Zhang, C Wu, S **a, F Zhao, M Gao… - … on Knowledge and …, 2023‏ - ieeexplore.ieee.org
Granular computing, a new paradigm for solving large-scale and complex problems, has
made significant progresses in knowledge discovery. Granular ball computing (GBC) is a …

Feature selection with harmony search

R Diao, Q Shen - IEEE Transactions on Systems, Man, and …, 2012‏ - ieeexplore.ieee.org
Many search strategies have been exploited for the task of feature selection (FS), in an effort
to identify more compact and better quality subsets. Such work typically involves the use of …

An efficient and accurate rough set for feature selection, classification, and knowledge representation

S **a, X Bai, G Wang, Y Cheng, D Meng… - … on Knowledge and …, 2022‏ - ieeexplore.ieee.org
This paper presents a strong data-mining method based on a rough set, which can
simultaneously realize feature selection, classification, and knowledge representation …