Kernel Dimensionality Reduction evaluation on various dimensions of effective subspaces for cancer patient survival analysis

Y. S. Chin, Ito Wasito, S. Z. Mohd Hashim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

In this research, we have extended the use of Kernel Dimensionality Reduction (KDR) in the context of semi supervised learning in particular for micro-array DNA clustering application. We have proposed a new model call K-means-KDR for survival analysis which we aimed to improve the genes classification performance and study the dimension of effective subspaces in cancer patient survival analysis. KDR method was extended and combined with the K-means clustering technique, Cox's proportional hazards regression model and log rank test where KDR contributes in gene classification to determine subgroups from the patient's group. Results from the experiments have indicated that our model has outperformed Support Vector Machines (SVM) in gene classification. We also observed that the best value for dimension of effective subspaces (K) for microarray DNA data is between 10%-20% of the total patients.

Original languageEnglish
Title of host publication10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
Pages790-793
Number of pages4
DOIs
Publication statusPublished - 23 Dec 2010
Event10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010 - Kuala Lumpur, Malaysia
Duration: 10 May 201013 May 2010

Publication series

Name10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010

Conference

Conference10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
Country/TerritoryMalaysia
CityKuala Lumpur
Period10/05/1013/05/10

Keywords

  • Dimension of Effective Subspaces (K)
  • Kernel Dimensionality Reduction (KDR)

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