Feature selection methods for big data bioinformatics: A survey from the search perspective

This paper surveys main principles of feature selection and their recent applications in big data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and embedded approaches to feature selection, we formulate feature selection as a combinatorial optimization or search p...

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Bibliographic Details
Published in:Methods (San Diego, Calif.) Vol. 111; pp. 21 - 31
Main Authors: Wang, Lipo, Wang, Yaoli, Chang, Qing
Format: Journal Article
Language:English
Published: United States Elsevier Inc 01.12.2016
Subjects:
Algorithms
Artificial Intelligence
bioinformatics
Biomarkers
Classification
Clustering
Computational biology
Computational Biology - methods
Computational Biology - trends
Computational intelligence
data collection
Data mining
Data Mining - methods
Data Mining - trends
Evolutionary algorithms
Evolutionary computation
Fuzzy logic
Genetic algorithms
Humans
Machine learning
Microarray
Neural networks
Particle swarm optimization
Pattern recognition
Random forests
Rough sets
Soft computing
Software
Support vector machines
surveys
Swarm intelligence
systematic review
Evolutionary computation
Evolutionary algorithms
Pattern recognition
Soft computing
Clustering
Data mining
Microarray
Particle swarm optimization
Random forests
Genetic algorithms
Fuzzy logic
Support vector machines
Neural networks
Rough sets
Classification
Machine learning
Biomarkers
Swarm intelligence
Computational intelligence
Computational biology
ISSN:1046-2023, 1095-9130
Online Access:Get full text
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Summary:This paper surveys main principles of feature selection and their recent applications in big data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and embedded approaches to feature selection, we formulate feature selection as a combinatorial optimization or search problem and categorize feature selection methods into exhaustive search, heuristic search, and hybrid methods, where heuristic search methods may further be categorized into those with or without data-distilled feature ranking measures.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-3
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ObjectType-Review-1
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ISSN:1046-2023
1095-9130
DOI:10.1016/j.ymeth.2016.08.014