Cancer feature selection and classification using a binary quantum-behaved particle swarm optimization and support vector machine

DOI10.1155/2016/3572705zbMath1359.92055DBLPjournals/cmmm/Xi0LFW16OpenAlexW2516481003WikidataQ37236125 ScholiaQ37236125MaRDI QIDQ519834

Yong-Cai Geng, Sumit K. Garg

Publication date: 7 April 2017

Published in: Computational \& Mathematical Methods in Medicine (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2016/3572705

zbMATH Keywords

support vector machinebinary quantum-behaved particle swarm optimizationeave-one-out cross validation

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Medical applications (general) (92C50)

Related Items (5)

Depression disorder classification of fMRI data using sparse low-rank functional brain network and graph-based features ⋮ Integrating data augmentation and hybrid feature selection for small sample credit risk assessment with high dimensionality ⋮ An improved binary quantum-behaved particle swarm optimization algorithm for knapsack problems ⋮ Feature genes selection using supervised locally linear embedding and correlation coefficient for microarray classification ⋮ Prediction of protein-protein interactions based on domain

Uses Software

• LIBSVM
• rda
• GeneSrF

Cites Work

• Unnamed Item
• Regularized linear discriminant analysis and its application in microarrays
• Incremental learning for \(\nu\)-support vector regression
• Least angle regression. (With discussion)
• Support-vector networks
• Gene selection from microarray data for cancer classification -- a machine learning approach
• Classification of gene microarrays by penalized logistic regression
• Gene selection for cancer classification using support vector machines

This page was built for publication: Cancer feature selection and classification using a binary quantum-behaved particle swarm optimization and support vector machine