A comprehensive survey on recent metaheuristics for feature selection
Feature selection has become an indispensable machine learning process for data
preprocessing due to the ever-increasing sizes in actual data. There have been many …
preprocessing due to the ever-increasing sizes in actual data. There have been many …
A comprehensive survey on sentiment analysis: Approaches, challenges and trends
Sentiment analysis (SA), also called Opinion Mining (OM) is the task of extracting and
analyzing people's opinions, sentiments, attitudes, perceptions, etc., toward different entities …
analyzing people's opinions, sentiments, attitudes, perceptions, etc., toward different entities …
Metaheuristic algorithms on feature selection: A survey of one decade of research (2009-2019)
Feature selection is a critical and prominent task in machine learning. To reduce the
dimension of the feature set while maintaining the accuracy of the performance is the main …
dimension of the feature set while maintaining the accuracy of the performance is the main …
Review of swarm intelligence-based feature selection methods
In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale datasets. On the other hand, data mining applications with high …
rapid growth of large-scale datasets. On the other hand, data mining applications with high …
[HTML][HTML] Benchmark for filter methods for feature selection in high-dimensional classification data
Feature selection is one of the most fundamental problems in machine learning and has
drawn increasing attention due to high-dimensional data sets emerging from different fields …
drawn increasing attention due to high-dimensional data sets emerging from different fields …
Advances in data preprocessing for biomedical data fusion: An overview of the methods, challenges, and prospects
Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Building an efficient intrusion detection system based on feature selection and ensemble classifier
Intrusion detection system (IDS) is one of extensively used techniques in a network topology
to safeguard the integrity and availability of sensitive assets in the protected systems …
to safeguard the integrity and availability of sensitive assets in the protected systems …
Feature selection in machine learning: A new perspective
High-dimensional data analysis is a challenge for researchers and engineers in the fields of
machine learning and data mining. Feature selection provides an effective way to solve this …
machine learning and data mining. Feature selection provides an effective way to solve this …
Feature selection using bare-bones particle swarm optimization with mutual information
X Song, Y Zhang, D Gong, X Sun - Pattern Recognition, 2021 - Elsevier
Feature selection (FS) is an important data processing method in pattern recognition and
data mining. Due to not considering characteristics of the FS problem itself, traditional …
data mining. Due to not considering characteristics of the FS problem itself, traditional …
A review of unsupervised feature selection methods
In recent years, unsupervised feature selection methods have raised considerable interest in
many research areas; this is mainly due to their ability to identify and select relevant features …
many research areas; this is mainly due to their ability to identify and select relevant features …