A robust ensemble classification method analysis

Zhang, Zhongwei ORCID: https://orcid.org/0000-0001-6622-0346 and Li, Jiuyong and Hu, Hong and Zhou, Hong (2010) A robust ensemble classification method analysis. In: Advances in computational biology. Advances in Experimental Medicine and Biology (680). Springer New York LLC, New York, NY. United States, pp. 149-155. ISBN 978-1-4419-5912-6

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Apart from the dimensionality problem, the uncertainty of Microarray data quality is another major challenge of Microarray classification. Microarray data contains various levels of noise and quite often are high levels of noise, and these data lead to unreliable and low accuracy analysis as well as the high dimensionality problem. In this paper, we propose a new Microarray data classification method, based on diversified multiple trees. The new method contains features that, (1) make most use of the information from the abundant genes in the Microarray data, and (2) use a unique diversity measurement in the ensemble decision committee. The experimental results show that the proposed classification method (DMDT) and the well known method (CS4), which diversifies trees by using distinct tree roots, are more accurate on average than other well-known ensemble methods, including Bagging, Boosting and Random Forests. The experiments also indicate that using diversity measurement of DMDT improves the classification accuracy of ensemble classification on Microarray data.

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Item Type: Book Chapter (Commonwealth Reporting Category B)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2010 Springer Science+Business Media, LLC. Permanent restricted access to published version due to publisher copyright policy. The book is composed of a collection of papers received in response to an announcement that was widely distributed to academicians and practitioners in the broad area of computational biology. Also, selected authors of accepted papers of BIOCOMP'09 proceedings (International Conference on Bioinformatics and Computational Biology: July 13-16 2009; Las Vegas, NV, USA) were invited to submit the extended versions of their papers for evaluation. Print copy held USQ Library 570.285 Adv.
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Date Deposited: 06 Jun 2011 23:11
Last Modified: 11 Nov 2014 01:29
Uncontrolled Keywords: microarray gene data; classification method; ensemble decision tree; diversity; accuracy
Fields of Research (2008): 01 Mathematical Sciences > 0103 Numerical and Computational Mathematics > 010399 Numerical and Computational Mathematics not elsewhere classified
06 Biological Sciences > 0604 Genetics > 060405 Gene Expression (incl. Microarray and other genome-wide approaches)
06 Biological Sciences > 0603 Evolutionary Biology > 060399 Evolutionary Biology not elsewhere classified
Fields of Research (2020): 49 MATHEMATICAL SCIENCES > 4903 Numerical and computational mathematics > 490399 Numerical and computational mathematics not elsewhere classified
31 BIOLOGICAL SCIENCES > 3105 Genetics > 310505 Gene expression (incl. microarray and other genome-wide approaches)
31 BIOLOGICAL SCIENCES > 3104 Evolutionary biology > 310499 Evolutionary biology not elsewhere classified
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970111 Expanding Knowledge in the Medical and Health Sciences
Identification Number or DOI: https://doi.org/10.1007/978-1-4419-4913-3_17
URI: http://eprints.usq.edu.au/id/eprint/8924

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