Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for ca...

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Detalles Bibliográficos
Autores principales: Xi, Maolong, Sun, Jun, Liu, Li, Fan, Fangyun, Wu, Xiaojun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2016
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5013239/
https://www.ncbi.nlm.nih.gov/pubmed/27642363
http://dx.doi.org/10.1155/2016/3572705

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5013239/
https://www.ncbi.nlm.nih.gov/pubmed/27642363
http://dx.doi.org/10.1155/2016/3572705

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