Attribute importance measures for supervised learning are important for improving both learning accuracy and interpretability. However, it is well-known there could be bias when th...
Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and relate...
Background: Random forests (RF) have been increasingly used in applications such as genome-wide association and microarray studies where predictor correlation is frequently observ...
Kristin K. Nicodemus, James D. Malley, Carolin Str...
Backbone variables have the same assignment in all solutions to a given constraint satisfaction problem; more generally, bias represents the proportion of solutions that assign a v...
Eric I. Hsu, Christian J. Muise, J. Christopher Be...
UCON is a highly flexible and expressive usage control model which allows an object owner to specify detailed usage control policies to be evaluated on a remote platform. Assuranc...
Mohammad Nauman, Masoom Alam, Xinwen Zhang, Tamlee...