L1 Splitting Rule in Survival Forests

• Activity Number: 369
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Hoora Moradian* and Denis Larocque and François Bellavance
• Companies: and HEC Montreal and HEC Montreal
• Keywords: Survival data ; right-censored data ; ensemble methods ; random forests ; survival forests

Abstract

The log-rank test is commonly used as the split function in many commonly used survival trees and forests algorithms. However, the log-rank test may have a significant loss of power in some circumstances, especially when the hazard functions or when the survival functions cross each other in the two compared groups. We investigate the use of the integrated absolute difference between the two children nodes survival functions as the splitting rule. Simulations studies and applications to real data sets show that forests built with this rule produce better results, compared to forests built with the log-rank splitting rule.