Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification

Microarray gene expression data are often accompanied by a large number of genes and a small number of samples. However, only a few of these genes are relevant to cancer, resulting in significant gene selection challenges. Hence, we propose a two-stage gene selection approach by combining extreme gr...

Full description

Saved in:
Bibliographic Details
Published in:Medical & biological engineering & computing Vol. 60; no. 3; pp. 663 - 681
Main Authors: Deng, Xiongshi, Li, Min, Deng, Shaobo, Wang, Lei
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2022
Springer Nature B.V
Subjects:
Algorithms
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Cancer
Classification
Computer Applications
Datasets
DNA microarrays
Feature selection
Gene expression
Genes
Genetic algorithms
Human Physiology
Humans
Imaging
Microarray Analysis
Multiple objective analysis
Neoplasms - genetics
Optimization
Original Article
Radiology
Multi-objective genetic algorithm
Microarray gene expression
Feature selection
XGBoost
Classification
ISSN:0140-0118, 1741-0444, 1741-0444
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Microarray gene expression data are often accompanied by a large number of genes and a small number of samples. However, only a few of these genes are relevant to cancer, resulting in significant gene selection challenges. Hence, we propose a two-stage gene selection approach by combining extreme gradient boosting (XGBoost) and a multi-objective optimization genetic algorithm (XGBoost-MOGA) for cancer classification in microarray datasets. In the first stage, the genes are ranked using an ensemble-based feature selection using XGBoost. This stage can effectively remove irrelevant genes and yield a group comprising the most relevant genes related to the class. In the second stage, XGBoost-MOGA searches for an optimal gene subset based on the most relevant genes’ group using a multi-objective optimization genetic algorithm. We performed comprehensive experiments to compare XGBoost-MOGA with other state-of-the-art feature selection methods using two well-known learning classifiers on 14 publicly available microarray expression datasets. The experimental results show that XGBoost–MOGA yields significantly better results than previous state-of-the-art algorithms in terms of various evaluation criteria, such as accuracy, F-score, precision, and recall. Graphical abstract
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0140-0118
1741-0444
1741-0444
DOI:10.1007/s11517-021-02476-x