Review of feature selection approaches based on grouping of features
With the rapid development in technology, large amounts of high-dimensional data have
been generated. This high dimensionality including redundancy and irrelevancy poses a …
been generated. This high dimensionality including redundancy and irrelevancy poses a …
A review of feature set partitioning methods for multi-view ensemble learning
A Kumar, J Yadav - Information Fusion, 2023 - Elsevier
Since the present era is entirely computer and Internet of Things (IoT) oriented, enormous
amounts of data are produced quickly from many sources. Machine learning's primary …
amounts of data are produced quickly from many sources. Machine learning's primary …
Feature grouping 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 …
Document-level event causality identification via graph inference mechanism
Event causality identification is an important research task in natural language processing.
Existing methods largely focus on identifying explicit causal relations, and give poor …
Existing methods largely focus on identifying explicit causal relations, and give poor …
Adaptive discriminant analysis for semi-supervised feature selection
As semi-supervised feature selection is becoming much more popular among researchers,
many related methods have been proposed in recent years. However, many of these …
many related methods have been proposed in recent years. However, many of these …
A hybrid feature selection approach for microarray datasets using graph theoretic-based method
The feature selection process plays an important role in different fields, particularly in
bioinformatics and microarray gene expression data analysis, for choosing discriminative …
bioinformatics and microarray gene expression data analysis, for choosing discriminative …
A novel predictive model of mixed oil length of products pipeline driven by traditional model and data
L Chen, Z Yuan, JX Xu, J Gao, Y Zhang… - Journal of Petroleum …, 2021 - Elsevier
Mixed oil will inevitably form during the batch transportation in products pipeline, and
accurate calculation of mixed oil length is of great significance for the economic benefit of …
accurate calculation of mixed oil length is of great significance for the economic benefit of …
Globally automatic fuzzy clustering for probability density functions and its application for image data
T Nguyen-Trang, T Nguyen-Thoi, T Vo-Van - Applied Intelligence, 2023 - Springer
Clustering for probability density functions (CDF) can be categorized as non-fuzzy and fuzzy
approaches. Regarding the second approach, the iterative refinement technique has been …
approaches. Regarding the second approach, the iterative refinement technique has been …
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 …
Fed-FiS: A novel information-theoretic federated feature selection for learning stability
In the era of big data and federated learning, traditional feature selection methods show
unacceptable performance for handling heterogeneity when deployed in federated …
unacceptable performance for handling heterogeneity when deployed in federated …