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Found 7 papers, 1 papers with code
Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees
Growth curve models are popular tools for studying the development of a response variable within subjects over time.
Imputation of missing values in multi-view data
This leads to very large quantities of missing data which, especially when combined with high-dimensionality, makes the application of conditional imputation methods computationally infeasible.
Improved prediction rule ensembling through model-based data generation
This article examines the use of surrogate modelsto improve performance of PRE, wherein the Lasso regression is trained with the help of a massivedataset generated by the (boosted) decision tree ensemble.
Analyzing hierarchical multi-view MRI data with StaPLR: An application to Alzheimer's disease classification
We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure.
View selection in multi-view stacking: Choosing the meta-learner
The remaining four meta-learners, namely nonnegative ridge regression, nonnegative forward selection, stability selection and the interpolating predictor, show little advantages in order to be preferred over the other three.
Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning
We compare the performance of StaPLR with an existing view selection method called the group lasso and observe that, in terms of view selection, StaPLR is often more conservative and has a consistently lower false positive rate.
pre: An R Package for Fitting Prediction Rule Ensembles
Prediction rule ensembles (PREs) are sparse collections of rules, offering highly interpretable regression and classification models.
Computation Methodology
