Heuristic Ranking Classification Method for Complex Large-Scale Survival Data

  • Nasser FardEmail author
  • Keivan Sadeghzadeh
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 360)


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.


ranking classification decision-making variable selection largescale data survival data 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Mechanical and Industrial EngineeringNortheastern UniversityBostonUSA

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