Genetic Programming Based Ensemble System for Microarray Data Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{Liu:2015:CMMM,

• author = "Kun-Hong Liu and Muchenxuan Tong and Shu-Tong Xie and Vincent To Yee Ng",
• title = "Genetic Programming Based Ensemble System for Microarray Data Classification",
• journal = "Computational and Mathematical Methods in Medicine",
• year = "2015",
• volume = "2015",
• pages = "Article ID 193406",
• keywords = "genetic algorithms, genetic programming",
• publisher = "Hindawi Publishing Corporation",
• bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov",
• language = "en",
• oai = "oai:pubmedcentral.nih.gov:4355811",
• rights = "Copyright 2015 Kun-Hong Liu et al.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.",
• URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC",
• ISSN = "1748-670X",
• URL = "http://downloads.hindawi.com/journals/cmmm/2015/193406.pdf",
• DOI = "doi:10.1155/2015/193406",
• size = "12 pages",
• abstract = "Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP) based new ensemble system (named GPES), which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved.",

}

Genetic Programming entries for Kun-Hong Liu MuChenxuan Tong Shu-Tong Xie Vincent To Yee Ng

Citations