Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel–Alsina Function
The COVID-19 pandemic led to an increase in healthcare waste (HCW). HCW management
treatment needs to be re-taken into focus to deal with this challenge. In practice, there are …
treatment needs to be re-taken into focus to deal with this challenge. In practice, there are …
A noise-aware fuzzy rough set approach for feature selection
Feature selection has aroused extensive attention and aims at selecting features that are
highly relevant to classification from raw datasets to improve the performance of a learning …
highly relevant to classification from raw datasets to improve the performance of a learning …
Feature grou** and selection with graph theory in robust fuzzy rough approximation space
Most extant feature selection works neglect interactive features in the form of groups, leading
to the omission of some important discriminative information. Moreover, the prevalence of …
to the omission of some important discriminative information. Moreover, the prevalence of …
R2CI: Information theoretic-guided feature selection with multiple correlations
Abstract Information theoretic-guided feature selection approaches (ITFSs), which exploit the
uncertainty of information to measure the correlation of features, aim to select the most …
uncertainty of information to measure the correlation of features, aim to select the most …
An in-depth and contrasting survey of meta-heuristic approaches with classical feature selection techniques specific to cervical cancer
S Kurman, S Kisan - Knowledge and Information Systems, 2023 - Springer
Data mining and machine learning algorithms' performance is degraded by data of high-
dimensional nature due to an issue called “curse of dimensionality”. Feature selection is a …
dimensional nature due to an issue called “curse of dimensionality”. Feature selection is a …
Feature selection using binary monarch butterfly optimization
L Sun, S Si, J Zhao, J Xu, Y Lin, Z Lv - Applied Intelligence, 2023 - Springer
Swarm intelligence algorithms have superior performance in searching for the optimal
feature subset, where Monarch Butterfly Optimization (MBO) can solve the continuous …
feature subset, where Monarch Butterfly Optimization (MBO) can solve the continuous …
Incremental feature selection approach to interval-valued fuzzy decision information systems based on λ-fuzzy similarity self-information
X Zhang, J Li - Information Sciences, 2023 - Elsevier
The relative decision self-information is a crucial evaluation function of feature selection in
information system. It encapsulates classification information in upper and lower …
information system. It encapsulates classification information in upper and lower …
Uncertainty instructed multi-granularity decision for large-scale hierarchical classification
Hierarchical classification identifies a sample from the root node to a leaf node along the
hierarchical structures of labels. It is often difficult to perform leaf-node prediction owing to …
hierarchical structures of labels. It is often difficult to perform leaf-node prediction owing to …
Feature selection considering multiple correlations based on soft fuzzy dominance rough sets for monotonic classification
Monotonic classification is a common task in the field of multicriteria decision-making, in
which features and decision obey a monotonic constraint. The dominance-based rough set …
which features and decision obey a monotonic constraint. The dominance-based rough set …
Incremental feature selection by sample selection and feature-based accelerator
Y Yang, D Chen, X Zhang, Z Ji, Y Zhang - Applied Soft Computing, 2022 - Elsevier
Incremental feature selection is an efficient paradigm that updates an optimal feature subset
from added-in data without forgetting the previously learned knowledge. Most existing …
from added-in data without forgetting the previously learned knowledge. Most existing …