Abstract
Outliers can have extreme influence on data analysis and so their presence must be taken into account. We propose a method to perform outlier detection on multivariate survival datasets, named Dual Bootstrap Hypothesis Testing (DBHT). Experimental results show that DBHT is a competitive alternative to state-of-the-art methods and can be applied to clinical data.
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Acknowledgments
Work supported by Fundação para a Ciência e a Tecnologia (FCT) under contracts LAETA (UID/EMS/50022/2013) and IT (UID/EEA/50008/2013), and by projects CancerSys (EXPL/EMS-SIS/1954/2013) and InteleGen (PTDC/DTP-FTO/1747/2012). SV acknowledges support by Program Investigador (IF/00653/2012) from FCT, co-funded by the European Social Fund through the Operational Program Human Potential.
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Pinto, J.D., Carvalho, A.M., Vinga, S. (2015). Outlier Detection in Cox Proportional Hazards Models Based on the Concordance c-Index. In: Pardalos, P., Pavone, M., Farinella, G., Cutello, V. (eds) Machine Learning, Optimization, and Big Data. MOD 2015. Lecture Notes in Computer Science(), vol 9432. Springer, Cham. https://doi.org/10.1007/978-3-319-27926-8_22
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DOI: https://doi.org/10.1007/978-3-319-27926-8_22
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