Feature selection methods on gene expression microarray data for cancer classification: A systematic review

E Alhenawi, R Al-Sayyed, A Hudaib… - Computers in biology and …, 2022 - Elsevier
This systematic review provides researchers interested in feature selection (FS) for
processing microarray data with comprehensive information about the main research …

Ensembles for feature selection: A review and future trends

V Bolón-Canedo, A Alonso-Betanzos - Information fusion, 2019 - Elsevier
Ensemble learning is a prolific field in Machine Learning since it is based on the assumption
that combining the output of multiple models is better than using a single model, and it …

Fedbcd: A communication-efficient collaborative learning framework for distributed features

Y Liu, X Zhang, Y Kang, L Li, T Chen… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
We introduce a novel federated learning framework allowing multiple parties having different
sets of attributes about the same user to jointly build models without exposing their raw data …

A survey on heterogeneous federated learning

D Gao, X Yao, Q Yang - arXiv preprint arXiv:2210.04505, 2022 - arxiv.org
Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

A federated feature selection algorithm based on particle swarm optimization under privacy protection

Y Hu, Y Zhang, X Gao, D Gong, X Song, Y Guo… - Knowledge-Based …, 2023 - Elsevier
Feature selection is an important preprocessing technique in the fields of data mining and
machine learning. With the promotion of privacy protection awareness, recently it becomes a …

Federated Learning and Differential Privacy: Software tools analysis, the Sherpa. ai FL framework and methodological guidelines for preserving data privacy

N Rodríguez-Barroso, G Stipcich, D Jiménez-López… - Information …, 2020 - Elsevier
The high demand of artificial intelligence services at the edges that also preserve data
privacy has pushed the research on novel machine learning paradigms that fit these …

Dynamic selection of normalization techniques using data complexity measures

S Jain, S Shukla, R Wadhvani - Expert Systems with Applications, 2018 - Elsevier
Data preprocessing is an important step for designing classification model. Normalization is
one of the preprocessing techniques used to handle the out-of-bounds attributes. This work …

Evolutionary computation for feature selection in classification: A comprehensive survey of solutions, applications and challenges

X Song, Y Zhang, W Zhang, C He, Y Hu, J Wang… - Swarm and Evolutionary …, 2024 - Elsevier
Feature selection (FS), as one of the most significant preprocessing techniques in the fields
of machine learning and pattern recognition, has received great attention. In recent years …

A hybrid mine blast algorithm for feature selection problems

M Alweshah, S Alkhalaileh, D Albashish, M Mafarja… - Soft Computing, 2021 - Springer
Feature selection (FS) is the process of finding the least possible number of features that are
able to describe a dataset in the same way as the original features. Feature selection is a …

Feature selection for online streaming high-dimensional data: A state-of-the-art review

EAK Zaman, A Mohamed, A Ahmad - Applied Soft Computing, 2022 - Elsevier
Abstract Knowledge discovery for data streaming requires online feature selection to reduce
the complexity of real-world datasets and significantly improve the learning process. This is …