期刊
METHODS
卷 111, 期 -, 页码 21-31出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ymeth.2016.08.014
关键词
Biomarkers; Classification; Clustering; Computational biology; Computational intelligence; Data mining; Evolutionary computation; Evolutionary algorithms; Fuzzy logic; Genetic algorithms; Machine learning; Microarray; Neural networks; Particle swarm optimization; Pattern recognition; Random forests; Rough sets; Soft computing; Swarm intelligence; Support vector machines
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. (C) 2016 Elsevier Inc. All rights reserved.
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