We propose a exible ensemble classification framework, Random Subspace Ensemble
(RaSE), for sparse classification. In the RaSE algorithm, we aggregate many weak learners …

Identifying interaction effects is fundamentally important in many scientific discoveries and
contemporary applications, but it is challenging since the number of pairwise interactions …

Variable screening has been a useful research area that deals with ultrahigh-dimensional
data. When there exist both marginally and jointly dependent predictors to the response …

In many scenarios such as genome-wide association studies where dependences between
variables commonly exist, it is often of interest to infer the interaction effects in the model …

The identification and understanding of gene-environment interactions can provide insights
into the pathways and mechanisms underlying complex diseases. However, testing for gene …

Datasets containing both categorical and continuous variables are frequently encountered
in many areas, and with the rapid development of modern measurement technologies, the …

We propose a new ensemble classification algorithm, named super random subspace
ensemble (Super RaSE), to tackle the sparse classification problem. The proposed …

Unprecedented advances in digital technology have produced a revolution that is
transforming science and society. Big data have been rapidly generated in many disciplines …

Feature screening has been proved to be effective and fashionable method for analyzing
high-dimensional data. It is a challenge in identifying important feature for the high …

We provide a detailed demo of the usage for the RaSEn package. This package implements
the random subspace ensemble classification (RaSE) method (Tian and Feng (2021b)), the …