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Kernel dimensionality reduction evaluation on various dimensions of effective subspaces for cancer patient survival analysis

Mohd. Hashim, Siti Zaiton and Chin, Y. S. and Wasito, Ito (2010) Kernel dimensionality reduction evaluation on various dimensions of effective subspaces for cancer patient survival analysis. In: International Conference on Information Science, Signal Processing and Their Applications (ISSPA 2010), 10-13 Mei 2010, Kuala Lumpur.

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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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Dimension of Effective Subspaces (K), Kernel Dimensionality Reduction (KDR)
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:24062
Deposited By: Liza Porijo
Deposited On:24 Sep 2012 05:27
Last Modified:24 Sep 2012 05:27

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