- Record: found
- Abstract: found
- Article: not found
Feature selection methods for big data bioinformatics: A survey from the search perspective.
Dec 01 2016
Biomarkers,
Classification,
Clustering,
Computational biology,
Computational intelligence,
Data mining,
Evolutionary algorithms,
Evolutionary computation,
Fuzzy logic,
Genetic algorithms,
Machine learning,
Microarray,
Neural networks,
Particle swarm optimization,
Pattern recognition,
Random forests,
Rough sets,
Soft computing,
Support vector machines,
Swarm intelligence
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
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.
Related collections
Author and article information
Support vector machines,Swarm intelligence,Classification,Clustering,Computational biology,Computational intelligence,Data mining,Evolutionary algorithms,Evolutionary computation,Fuzzy logic,Genetic algorithms,Machine learning,Microarray,Neural networks,Particle swarm optimization,Pattern recognition,Random forests,Rough sets,Soft computing,Biomarkers
Support vector machines, Swarm intelligence, Classification, Clustering, Computational biology, Computational intelligence, Data mining, Evolutionary algorithms, Evolutionary computation, Fuzzy logic, Genetic algorithms, Machine learning, Microarray, Neural networks, Particle swarm optimization, Pattern recognition, Random forests, Rough sets, Soft computing, Biomarkers