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 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.
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Electronic address: elpwang@ntu.edu.sg.
Lipo
L
Wang
College of Information Engineering, Taiyuan University of Technology, Taiyuan, China. Electronic address: wangyaoli@tyut.edu.cn.
Yaoli
Y
Wang
College of Information Engineering, Taiyuan University of Technology, Taiyuan, China. Electronic address: changqing@tyut.edu.cn.
Qing
Q
Chang
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods* / Q000639:trends; D057225:Data Mining / Q000379:methods* / Q000639:trends; D006801:Humans; D012984:Software
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