Quantifying the similarity between two or more datasets is an important task in statistics and machine learning. In meta-learning, it enables the transfer of knowledge across tasks and datasets. In simulation studies, the similarity between the distributions assumed in the simulation and the distributions of the datasets for which the performance of methods is assessed is crucial. Similarly,...
The assessment of crop variety distinctness, uniformity, and stability (DUS) is a fundamental component of plant breeding and registration processes. Traditionally, one-dimensional analysis of variance is conducted separately for each attribute. However, before conducting separate analyses, it would be worthwhile to apply multivariate methods to determine whether a given variety differs from...
The aim of the work is to find important characterizations of mixture of experts which have
an impact on improvement of combined classifier performance over the averaged
performance of the base learners. The problem was examined for various high
dimensional genomic data sets.
Mixture of experts are useful for responses differentiating among base classifiers.
From this point of view...
Background:
A random forest (RF) is an efficient method for prediction but it is difficult to
interpret.
Artificial Representative Trees (ARTs) are a special type of surrogate model
that approximates the original strucutre of the RF in a single tree, achieving
similar predictive accuracy.
Conformal Predictive Systems (CPS) provide a framework for uncertainty
quantification by generating...
Sharing of original study data may be restricted by data protection policies. Instead, synthetic data that mimics the original data structure may be shared between research groups. This work introduces modgo 2.0 which may be used for generating synthetic data from existing study data. Simulations may be based either on the combination of the rank inverse normal transformation with simulation...
