Hessian-based semi-supervised feature selection using generalized uncorrelated constraint
Feature selection (FS) aims to eliminate redundant features and choose the informative
ones. Since labeled data are not always easily available and abundant unlabeled data are …
ones. Since labeled data are not always easily available and abundant unlabeled data are …
Impact of land use on water quality in buffer zones at different scales in the Poyang Lake, middle reaches of the Yangtze River basin
The water quality of Poyang Lake (PYL) is significantly influenced by land use which is a
crucial factor that can exhibit complex changes in the environment and can serve as an …
crucial factor that can exhibit complex changes in the environment and can serve as an …
A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of heart-related diseases
This study aims to use a machine learning (ML)-based enhanced diagnosis and survival
model to predict heart disease and survival in heart failure by combining the cuckoo search …
model to predict heart disease and survival in heart failure by combining the cuckoo search …
Graph embedding orthogonal decomposition: A synchronous feature selection technique based on collaborative particle swarm optimization
In unsupervised feature selection, the clustering label matrix has the ability to distinguish
between projection clusters. However, the latent geometric structure of the clustering labels …
between projection clusters. However, the latent geometric structure of the clustering labels …
A principal label space transformation and ridge regression-based hybrid gorilla troops optimization and jellyfish search algorithm for multi-label classification
SHS Ebrahimi, K Majidzadeh, FS Gharehchopogh - Cluster Computing, 2024 - Springer
Classification as an essential part of Machine Learning and Data Mining has significant
roles in engineering, medicine, agriculture, military, etc. With the evolution of data collection …
roles in engineering, medicine, agriculture, military, etc. With the evolution of data collection …
NSOFS: a non-dominated sorting-based online feature selection algorithm
Online streaming feature selection (OSFS) methods are used to dynamically update the
feature space as well as remove irrelevant and redundant features from the data. Since most …
feature space as well as remove irrelevant and redundant features from the data. Since most …
Binary dynamic stochastic search algorithm with support vector regression for feature selection in low-velocity impact localization problem
Q Liu, F Wang, W Xiao, J Cui - Engineering Applications of Artificial …, 2023 - Elsevier
Locating low-velocity impacts (LVIs) on composite plates precisely is necessary. Support
vector regression (SVR) is an effective method in addressing the LVI localization problem …
vector regression (SVR) is an effective method in addressing the LVI localization problem …
A two-layer approach for the decentralized multi-project scheduling problem sharing multi-skilled staff
W You, Z Xu, S Zhao - IEEE Access, 2024 - ieeexplore.ieee.org
With the rapid development of economic globalization and information technology, the
projects undertaken by enterprises are gradually becoming larger and more complex. The …
projects undertaken by enterprises are gradually becoming larger and more complex. The …
A Streaming Feature Selection Method Based on Dynamic Feature Clustering and Particle Swarm Optimization
X Song, H Ma, Y Zhang, D Gong… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Feature selection is an effective data preprocessing technique. In some practical
applications, features may continuously arrive one by one or by groups, and we cannot …
applications, features may continuously arrive one by one or by groups, and we cannot …
Multi-class nonparallel support vector machine
A Sahleh, M Salahi, S Eskandari - Progress in Artificial Intelligence, 2023 - Springer
In this paper, we propose an extended version of Nonparallel Support Vector Machine
(NPSVM) for multi-classification using one-versus-one-versus-rest approach called …
(NPSVM) for multi-classification using one-versus-one-versus-rest approach called …