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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol. 4(6): 483-490 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V6.459

Machine Learning Approaches to Survival Analysis: Case Studies in Microarray for Breast Cancer

Liu Yang and Kristiaan Pelckmans
Abstract—Cox Proportional Hazard model and the associated Partial Likelihood criterion have been the main tool of inference for data of survival studies. There are many powerful techniques in modern machine learning which could be applied on the survival studies and increase the performance. This paper consider the problem of comparing different techniques for inference with large dimensional data of breast cancer. Besides an overview of the different techniques, numerical experiments are presented. Liu Yang managed to implement the algorithms and the experiments. Kristiaan Pelckmans managed to complete the theoretical part.

Index Terms—Machine learning, survival analysis, boost-ing, SVM.

Liu Yang is with the Department of Information Technology, Uppsala Universtiy, 75105 Uppsala, Sweden (e-mail: sjtuly@gmail.com). Kristiaan Pelckmans is with the Department of Information Technology, Uppsala Universtiy, 75105 Uppsala, Sweden.

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Cite: Liu Yang and Kristiaan Pelckmans, "Machine Learning Approaches to Survival Analysis: Case Studies in Microarray for Breast Cancer," International Journal of Machine Learning and Computing vol. 4, no. 6, pp. 483-490, 2014.

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