Penalty-based cluster validity index for class discovery from cancer data

Zhiwen Yu; Jia You; Guihua Wen

doi:10.1109/ICMLC.2011.6017005

Penalty-based cluster validity index for class discovery from cancer data

Zhiwen Yu, Jia You, Guihua Wen

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

In order to perform successful diagnosis and treatment of cancer, discovering and classifying cancer types correctly is essential. One of the challenges in cancer class discovery is to estimate the number of classes given a set of unknown microarray data. In the paper, we propose a new cluster validity criterion called Penalty-based Disagreement Index (PDI) based on the perturbation technique to estimate the number of classes in microarray data, PDI not only considers the disagreement between the partition results obtained from the original data and those obtained from the perturbed data, but also includes a penalty measure which is a function of the number of classes. Our experiments show that PDI successfully estimates the true number of classes in a number of challenging real cancer datasets.

Original language	English
Title of host publication	Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Pages	1577-1582
Number of pages	6
Volume	4
DOIs	https://doi.org/10.1109/ICMLC.2011.6017005
Publication status	Published - 7 Nov 2011
Event	2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China Duration: 10 Jul 2011 → 13 Jul 2011

Conference

Conference	2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Country/Territory	China
City	Guilin, Guangxi
Period	10/07/11 → 13/07/11

Keywords

Cancer data
Class discovery
Cluster ensemble

ASJC Scopus subject areas

Artificial Intelligence
Computational Theory and Mathematics
Computer Networks and Communications
Human-Computer Interaction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

More information

10.1109/ICMLC.2011.6017005

Cite this

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abstract = "In order to perform successful diagnosis and treatment of cancer, discovering and classifying cancer types correctly is essential. One of the challenges in cancer class discovery is to estimate the number of classes given a set of unknown microarray data. In the paper, we propose a new cluster validity criterion called Penalty-based Disagreement Index (PDI) based on the perturbation technique to estimate the number of classes in microarray data, PDI not only considers the disagreement between the partition results obtained from the original data and those obtained from the perturbed data, but also includes a penalty measure which is a function of the number of classes. Our experiments show that PDI successfully estimates the true number of classes in a number of challenging real cancer datasets.",

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Yu, Z, You, J & Wen, G 2011, Penalty-based cluster validity index for class discovery from cancer data. in Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011. vol. 4, 6017005, pp. 1577-1582, 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011, Guilin, Guangxi, China, 10/07/11. https://doi.org/10.1109/ICMLC.2011.6017005

Penalty-based cluster validity index for class discovery from cancer data. / Yu, Zhiwen; You, Jia; Wen, Guihua.

Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011. Vol. 4 2011. p. 1577-1582 6017005.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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N2 - In order to perform successful diagnosis and treatment of cancer, discovering and classifying cancer types correctly is essential. One of the challenges in cancer class discovery is to estimate the number of classes given a set of unknown microarray data. In the paper, we propose a new cluster validity criterion called Penalty-based Disagreement Index (PDI) based on the perturbation technique to estimate the number of classes in microarray data, PDI not only considers the disagreement between the partition results obtained from the original data and those obtained from the perturbed data, but also includes a penalty measure which is a function of the number of classes. Our experiments show that PDI successfully estimates the true number of classes in a number of challenging real cancer datasets.

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Penalty-based cluster validity index for class discovery from cancer data

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

More information

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