Feature subset selection for data and feature streams: a review
Real-world problems are commonly characterized by a high feature dimensionality, which
hinders the modelling and descriptive analysis of the data. However, some of these data …
hinders the modelling and descriptive analysis of the data. However, some of these data …
A tutorial-based survey on feature selection: Recent advancements on feature selection
A Moslemi - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Curse of dimensionality is known as big challenges in data mining, pattern recognition,
computer vison and machine learning in recent years. Feature selection and feature …
computer vison and machine learning in recent years. Feature selection and feature …
Comparative study on total nitrogen prediction in wastewater treatment plant and effect of various feature selection methods on machine learning algorithms …
F Bagherzadeh, MJ Mehrani, M Basirifard… - Journal of Water Process …, 2021 - Elsevier
Wastewater characteristics prediction in wastewater treatment plants (WWTPs) is valuable
and can reduce the number of sampling, energy, and cost. Feature Selection (FS) methods …
and can reduce the number of sampling, energy, and cost. Feature Selection (FS) methods …
Learning correlation information for multi-label feature selection
In many real-world multi-label applications, the content of multi-label data is usually
characterized by high dimensional features, which contains complex correlation information …
characterized by high dimensional features, which contains complex correlation information …
Group-preserving label-specific feature selection for multi-label learning
In many real-world application domains, eg, text categorization and image annotation,
objects naturally belong to more than one class label, giving rise to the multi-label learning …
objects naturally belong to more than one class label, giving rise to the multi-label learning …
MFS-MCDM: Multi-label feature selection using multi-criteria decision making
A Hashemi, MB Dowlatshahi… - Knowledge-Based …, 2020 - Elsevier
In this paper, for the first time, a feature selection procedure is modeled as a multi-criteria
decision making (MCDM) process. This method is applied to a multi-label data and we have …
decision making (MCDM) process. This method is applied to a multi-label data and we have …
Noise-resistant multilabel fuzzy neighborhood rough sets for feature subset selection
Feature selection attempts to capture the more discriminative features and plays a significant
role in multilabel learning. As an efficient mathematical tool to handle incomplete and …
role in multilabel learning. As an efficient mathematical tool to handle incomplete and …
Feature-specific mutual information variation for multi-label feature selection
Recent years has witnessed urgent needs for addressing the curse of dimensionality
regarding multi-label data, which attracts wide attention for feature selection. Feature …
regarding multi-label data, which attracts wide attention for feature selection. Feature …
MFSJMI: Multi-label feature selection considering join mutual information and interaction weight
P Zhang, G Liu, J Song - Pattern Recognition, 2023 - Elsevier
Multi-label feature selection captures a reliable and informative feature subset from high-
dimensional multi-label data, which plays an important role in pattern recognition. In …
dimensional multi-label data, which plays an important role in pattern recognition. In …
Ant-TD: Ant colony optimization plus temporal difference reinforcement learning for multi-label feature selection
M Paniri, MB Dowlatshahi… - Swarm and Evolutionary …, 2021 - Elsevier
In recent years, multi-label learning becomes a trending topic in machine learning and data
mining. This type of learning deals with data that each instance is associated with more than …
mining. This type of learning deals with data that each instance is associated with more than …