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

Lipo Wang¹, Yaoli Wang², Qing Chang²

¹School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.

Methods (San Diego, Calif.)

|September 5, 2016

Summary

This study re-frames feature selection in big data bioinformatics as a search problem, categorizing methods into exhaustive, heuristic, and hybrid approaches for better analysis.

Keywords:

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

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Area of Science:

Bioinformatics
Computational Biology
Data Science

Background:

Feature selection is crucial for analyzing large biological datasets.
Traditional categorization (filter, wrapper, embedded) has limitations in big data contexts.

Purpose of the Study:

To survey feature selection principles and applications in big data bioinformatics.
To propose an alternative categorization of feature selection methods.

Main Methods:

Re-framing feature selection as a combinatorial optimization or search problem.
Categorizing methods into exhaustive search, heuristic search, and hybrid methods.
Further classifying heuristic methods based on data-distilled feature ranking.

Main Results:

Identified limitations of traditional feature selection categorizations for big data.
Proposed a novel framework for understanding feature selection methodologies.
Highlighted the importance of search and optimization paradigms.

Conclusions:

The proposed categorization offers a more comprehensive view of feature selection techniques.
This framework aids in understanding and applying feature selection in big data bioinformatics.
Future research can build upon this search-problem formulation.