A hybrid feature selection algorithm for gene expression data classification

Huijuan Lu; Junying Chen; Ke Yan; Qun Jin; Yu Xue; Zhigang Gao

doi:10.1016/j.neucom.2016.07.080

ScienceGate Book Chapters

JOURNAL ARTICLE

A hybrid feature selection algorithm for gene expression data classification

Huijuan Lu Junying Chen Ke Yan Qun Jin Yu Xue Zhigang Gao

Year: 2017 Journal: Neurocomputing Vol: 256 Pages: 56-62 Publisher: Elsevier BV

DOI: 10.1016/j.neucom.2016.07.080

Get Full-Text PDF Get Analytical Report

Keywords:

Feature selection Computer science Robustness (evolution) Pattern recognition (psychology) Gene selection Artificial intelligence Mutual information Minimum redundancy feature selection Data mining Feature (linguistics) Selection (genetic algorithm) Algorithm Microarray analysis techniques Gene Gene expression Biology Genetics

Metrics

349

Cited By

15.37

FWCI (Field Weighted Citation Impact)

Refs

0.99

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Gene expression and cancer classification

Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology

Evolutionary Algorithms and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Face and Expression Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

A hybrid feature selection algorithm for gene expression data classification

Metrics

Citation History

Topics

Related Documents

Gene expression data classification using genetic algorithm-based feature selection

Hybrid Feature Selection Algorithm mRMR-ICA for Cancer Classification from Microarray Gene Expression Data

Review on hybrid feature selection and classification of microarray gene expression data

An Integrated Feature Selection Algorithm for Cancer Classification using Gene Expression Data

Microarray Gene Expression Classification: An Efficient Feature Selection Using Hybrid Swarm Intelligence Algorithm