Machine Learning
Shape quantization and recognition with randomized trees
Neural Computation
Machine Learning
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm
The Journal of Machine Learning Research
Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods
The Journal of Machine Learning Research
Recursive partitioning on incomplete data using surrogate decisions and multiple imputation
Computational Statistics & Data Analysis
A new variable selection approach using Random Forests
Computational Statistics & Data Analysis
Hi-index | 0.03 |
Random Forests in combination with Stability Selection allow to estimate stable conditional independence graphs with an error control mechanism for false positive selection. This approach is applicable to graphs containing both continuous and discrete variables at the same time. Its performance is evaluated in various simulation settings and compared with alternative approaches. Finally, the approach is applied to two heath-related data sets, first to study the interconnection of functional health components, personal, and environmental factors and second to identify risk factors which may be associated with adverse neurodevelopment after open-heart surgery.