An ensemble approach for phenotype classification based on fuzzy partitioning of gene expression data

A. Dragomir; I. Maraziotis; A. Bezerianos

doi:10.1109/IEMBS.2006.259348

An ensemble approach for phenotype classification based on fuzzy partitioning of gene expression data

A. Dragomir, I. Maraziotis, A. Bezerianos

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

We focus on developing a pattern recognition method suitable for performing supervised analysis tasks on molecular data resulting from microarray experiments. Molecular characterization of tissue samples using microarray gene expression profiling is expected to uncover fundamental aspects related to cancer diagnosis and drug discovery. There is therefore a need for reliable, accurate classification methods. With this study we propose a framework for constructing an ensemble of individually trained SVM classifiers, each of them specialized on subsets of the input space. The fuzzy approach used for partitioning the data produces overlapping subsets of the input space that facilitates subsequent classification tasks.

Original language	English (US)
Title of host publication	28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Pages	5834-5837
Number of pages	4
DOIs	https://doi.org/10.1109/IEMBS.2006.259348
State	Published - Dec 1 2006
Event	28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States Duration: Aug 30 2006 → Sep 3 2006

Publication series

Name	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ISSN (Print)	0589-1019

Other

Other	28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Country/Territory	United States
City	New York, NY
Period	8/30/06 → 9/3/06

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/IEMBS.2006.259348

Cite this

Dragomir, A., Maraziotis, I., & Bezerianos, A. (2006). An ensemble approach for phenotype classification based on fuzzy partitioning of gene expression data. In 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 (pp. 5834-5837). Article 4029429 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings). https://doi.org/10.1109/IEMBS.2006.259348

An ensemble approach for phenotype classification based on fuzzy partitioning of gene expression data. / Dragomir, A.; Maraziotis, I.; Bezerianos, A.
28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06. 2006. p. 5834-5837 4029429 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Dragomir, A, Maraziotis, I & Bezerianos, A 2006, An ensemble approach for phenotype classification based on fuzzy partitioning of gene expression data. in 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06., 4029429, Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, pp. 5834-5837, 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06, New York, NY, United States, 8/30/06. https://doi.org/10.1109/IEMBS.2006.259348

Dragomir A, Maraziotis I, Bezerianos A. An ensemble approach for phenotype classification based on fuzzy partitioning of gene expression data. In 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06. 2006. p. 5834-5837. 4029429. (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings). doi: 10.1109/IEMBS.2006.259348

Dragomir, A. ; Maraziotis, I. ; Bezerianos, A. / An ensemble approach for phenotype classification based on fuzzy partitioning of gene expression data. 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06. 2006. pp. 5834-5837 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings).

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An ensemble approach for phenotype classification based on fuzzy partitioning of gene expression data

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