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A weighted random survival forest
- Source :
- Knowledge-Based Systems. 177:136-144
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- A weighted random survival forest is presented in the paper. It can be regarded as a modification of the random forest improving its performance. The main idea underlying the proposed model is to replace the standard procedure of averaging used for estimation of the random survival forest hazard function by weighted averaging where the weights are assigned to every tree and can be viewed as training parameters which are computed in an optimal way by solving a standard quadratic optimization problem maximizing Harrell’s C-index. Numerical examples with real data illustrate the outperformance of the proposed model in comparison with the original random survival forest.
- Subjects :
- FOS: Computer and information sciences
Hazard (logic)
Computer Science - Machine Learning
Information Systems and Management
Computer science
Machine Learning (stat.ML)
02 engineering and technology
Function (mathematics)
Tree (graph theory)
Machine Learning (cs.LG)
Management Information Systems
Random forest
Statistics - Machine Learning
Artificial Intelligence
020204 information systems
Statistics
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Quadratic programming
Software
Subjects
Details
- ISSN :
- 09507051
- Volume :
- 177
- Database :
- OpenAIRE
- Journal :
- Knowledge-Based Systems
- Accession number :
- edsair.doi.dedup.....cf574f738a658884452cd6ea0a85bc8b