Abstract
Unlike traditional datasets with a few explanatory variables, analysis of datasets with high number of explanatory variables requires different approaches. Determining effective explanatory variables, specifically in a complex and large-scale data provides an excellent opportunity to increase efficiency and reduce costs. In a large-scale data with many variables, a variable selection technique could be used to specify a subset of explanatory variables that are significantly more valuable to analyze specially in the survival data analysis. A heuristic variable selection method through ranking classification to analyze large-scale survival data which reduces redundant information and facilitates practical decision-making by evaluating variable efficiency (the correlation of variable and survival time) is presented. A numerical simulation experiment is developed to investigate the performance and validation of the proposed method.
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Fard, N., Sadeghzadeh, K. (2015). Heuristic Ranking Classification Method for Complex Large-Scale Survival Data. In: Le Thi, H., Pham Dinh, T., Nguyen, N. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. Advances in Intelligent Systems and Computing, vol 360. Springer, Cham. https://doi.org/10.1007/978-3-319-18167-7_5
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DOI: https://doi.org/10.1007/978-3-319-18167-7_5
Publisher Name: Springer, Cham
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