Name
Abstract | ||
|---|---|---|
Survival data is common in medical applications. The challenge in applying predictive data-analytic methods to survival data is in the treatment of censored observations, since the survival times for these observations are unknown. This paper presents formalization of the analysis of survival data as a binary classification problem. For this binary classification setting, we propose a strategy for encoding censored data, leading to the SVM+/LUPI formulations. Further, we present empirical comparison of the new method and the classical Cox modeling approach for predictive modeling of survival data. These comparisons suggest that for data sets with large amount of censored data, the proposed method consistently yields better predictive performance than classical statistical modeling. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IJCNN.2014.6889517 | IJCNN |
Keywords | Field | DocType |
medical information systems,medical applications,censored data encoding,learning (artificial intelligence),binary classification problem,pattern classification,svm-lupi formulations,data analysis,learning using privileged information,predictive modeling,predictive data-analytic methods,survival data modeling,classical cox modeling approach,support vector machines,data models,predictive models,learning artificial intelligence,kernel,training data | Pattern recognition,Survival data,Computer science,Artificial intelligence,Machine learning | Conference |
ISSN | Citations | PageRank |
2161-4393 | 6 | 0.59 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han-Tai Shiao | 1 | 16 | 1.37 |
| Vladimir Cherkassky | 2 | 1064 | 126.66 |