Evolutionary computation for feature selection in classification: A comprehensive survey of solutions, applications and challenges
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 …
of machine learning and pattern recognition, has received great attention. In recent years …
Multiobjective particle swarm optimization for feature selection with fuzzy cost
Y Hu, Y Zhang, D Gong - IEEE Transactions on Cybernetics, 2020 - ieeexplore.ieee.org
Feature selection (FS) is an important data processing technique in the field of machine
learning. There have been various FS methods, but all assume that the cost associated with …
learning. There have been various FS methods, but all assume that the cost associated with …
RUL prediction of machinery using convolutional-vector fusion network through multi-feature dynamic weighting
Based on the features extracted from the condition monitoring data, data-driven prognostic
approaches are able to predict the remaining useful life (RUL) of machinery. Existing …
approaches are able to predict the remaining useful life (RUL) of machinery. Existing …
Clustering-guided particle swarm feature selection algorithm for high-dimensional imbalanced data with missing values
Y Zhang, YH Wang, DW Gong… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Feature selection (FS) in data with class imbalance or missing values has received much
attention from researchers due to their universality in real-world applications. However, for …
attention from researchers due to their universality in real-world applications. However, for …
Geometric structural ensemble learning for imbalanced problems
The classification on imbalanced data sets is a great challenge in machine learning. In this
paper, a geometric structural ensemble (GSE) learning framework is proposed to address …
paper, a geometric structural ensemble (GSE) learning framework is proposed to address …
Integrating TANBN with cost sensitive classification algorithm for imbalanced data in medical diagnosis
D Gan, J Shen, B An, M Xu, N Liu - Computers & Industrial Engineering, 2020 - Elsevier
For the imbalanced classification problems, most traditional classification models only focus
on searching for an excellent classifier to maximize classification accuracy with the fixed …
on searching for an excellent classifier to maximize classification accuracy with the fixed …
Joint imbalanced classification and feature selection for hospital readmissions
Hospital readmission is one of the most important service quality measures. Recently,
numerous risk assessment models have been proposed to address the hospital readmission …
numerous risk assessment models have been proposed to address the hospital readmission …
Cost-sensitive learning
Cost-sensitive learning is an aspect of algorithm-level modifications for class imbalance.
Here, instead of using a standard error-driven evaluation (or 0–1 loss function), a …
Here, instead of using a standard error-driven evaluation (or 0–1 loss function), a …
Binary atom search optimisation approaches for feature selection
J Too, A Rahim Abdullah - Connection Science, 2020 - Taylor & Francis
Atom Search Optimisation (ASO) is a recently proposed metaheuristic algorithm that has
proved to work effectively on several benchmark tests. In this paper, we propose the binary …
proved to work effectively on several benchmark tests. In this paper, we propose the binary …
A heterogeneous ensemble learning framework for spam detection in social networks with imbalanced data
C Zhao, Y Xin, X Li, Y Yang, Y Chen - Applied Sciences, 2020 - mdpi.com
The popularity of social networks provides people with many conveniences, but their rapid
growth has also attracted many attackers. In recent years, the malicious behavior of social …
growth has also attracted many attackers. In recent years, the malicious behavior of social …