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Found 7 papers, 0 papers with code
Challenges in Variable Importance Ranking Under Correlation
Recently, several adjustments to marginal permutation utilizing feature knockoffs were proposed to address this issue, such as the variable importance measure known as conditional predictive impact (CPI).
Multi-dimension unified Swin Transformer for 3D Lesion Segmentation in Multiple Anatomical Locations
In oncology research, accurate 3D segmentation of lesions from CT scans is essential for the modeling of lesion growth kinetics.
Target alignment in truncated kernel ridge regression
We show that for polynomial alignment, there is an \emph{over-aligned} regime, in which TKRR can achieve a faster rate than what is achievable by full KRR.
A Framework for an Assessment of the Kernel-target Alignment in Tree Ensemble Kernel Learning
Kernels ensuing from tree ensembles such as random forest (RF) or gradient boosted trees (GBT), when used for kernel learning, have been shown to be competitive to their respective tree ensembles (particularly in higher dimensional scenarios).
(Decision and regression) tree ensemble based kernels for regression and classification
We elucidate the performance and properties of the RF and GBT based kernels in a comprehensive simulation study comprising of continuous and binary targets.
Random Forest (RF) Kernel for Regression, Classification and Survival
We elucidate the performance and properties of the data driven RF kernels used by regularized linear models in a comprehensive simulation study comprising of continuous, binary and survival targets.
A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials
Herein we propose a deep learning-based approach for the prediction of lung lesion response based on radiomic features extracted from clinical CT scans of patients in non-small cell lung cancer trials.
