A Comprehensive Survey on Recent Feature Selection Methods for Mixed Data: Challenges, Solutions and Future Directions
ME Warkiani, MH Moattar - Neurocomputing, 2025 - Elsevier
Feature selection plays a crucial role in data analysis, pattern recognition, and machine
learning by reducing feature dimensions, improving execution time, and enhancing model …
learning by reducing feature dimensions, improving execution time, and enhancing model …
An improved high-dimensional kriging surrogate modeling method through principal component dimension reduction
Y Li, J Shi, Z Yin, J Shen, Y Wu, S Wang - Mathematics, 2021 - mdpi.com
The Kriging surrogate model in complex simulation problems uses as few expensive
objectives as possible to establish a global or local approximate interpolation. However, due …
objectives as possible to establish a global or local approximate interpolation. However, due …
Hybrid similarity relation based mutual information for feature selection in intuitionistic fuzzy rough framework and its applications
Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been
effectively and efficiently applied for feature selection to handle the uncertainty in real …
effectively and efficiently applied for feature selection to handle the uncertainty in real …
Performance evaluation of ingenious crow search optimization algorithm for protein structure prediction
Protein structure prediction is one of the important aspects while dealing with critical
diseases. An early prediction of protein folding helps in clinical diagnosis. In recent years …
diseases. An early prediction of protein folding helps in clinical diagnosis. In recent years …
Using topological data analysis and machine learning to predict customer churn
Customer churn is a common problem faced by many industries, including
telecommunication industries. This has resulted in the development of advanced techniques …
telecommunication industries. This has resulted in the development of advanced techniques …
[PDF][PDF] Data Mining Approach to Analyze COVID-19 Dataset of Mexican Patients
W Almayyan - International Journal of Computer Applications - academia.edu
The pandemic originated by coronavirus (COVID-19), force governments to choosing
different health policies to stop the infection and inspire many research groups to work on …
different health policies to stop the infection and inspire many research groups to work on …
Improving projection of Deep learning-based Precipitation in India using Dimensionality Reduction Technique
N Mehta, A Thakkar, H Shashtri… - … Computing and Data …, 2022 - ieeexplore.ieee.org
Climate change is surely one of the major concerns in the 21st century. Various research
efforts to understand the future of the world's climate are being made. The climate …
efforts to understand the future of the world's climate are being made. The climate …
[PDF][PDF] An Improved High-Dimensional Kriging Surrogate Modeling Method through Principal Component Dimension Reduction. Mathematics 2021, 9, 1985
Y Li, J Shi, Z Yin, J Shen, Y Wu, S Wang - Modeling and Simulation in …, 2021 - core.ac.uk
The Kriging surrogate model in complex simulation problems uses as few expensive
objectives as possible to establish a global or local approximate interpolation. However, due …
objectives as possible to establish a global or local approximate interpolation. However, due …