Optimal survival trees ensemble
Naz Gul, Nosheen Faiz, Dan Brawn, Rafal Kulakowski, Zardad Khan and, Berthold Lausen

TL;DR
This paper introduces an optimal survival trees ensemble (OSTE) method that selects the most predictive trees based on error ranking and performance improvement, enhancing accuracy and reducing model complexity.
Contribution
The paper proposes a novel ensemble method for survival analysis that optimally selects trees based on error ranking and performance, outperforming existing methods.
Findings
OSTE improves predictive accuracy over traditional methods.
The ensemble reduces the number of trees needed for optimal performance.
Validated on 17 benchmark datasets.
Abstract
Recent studies have adopted an approach of selecting accurate and diverse trees based on individual or collective performance within an ensemble for classification and regression problems. This work follows in the wake of these investigations and considers the possibility of growing a forest of optimal survival trees. Initially, a large set of survival trees are grown using the method of random survival forest. The grown trees are then ranked from smallest to highest value of their prediction error using out-of-bag observations for each respective survival tree. The top ranked survival trees are then assessed for their collective performance as an ensemble. This ensemble is initiated with the survival tree which stands first in rank, then further trees are tested one by one by adding them to the ensemble in order of rank. A survival tree is selected for the resultant ensemble if the…
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Taxonomy
TopicsData Mining Algorithms and Applications · Data Management and Algorithms · Advanced Database Systems and Queries
