In contrast to canonical redundancy analysis, which examines how well the original variables can be predicted from the canonical variables, maximum redundancy analysis finds the variables that can ...

In the development of data-driven models for streamflow forecasting, choosing appropriate input variables is crucial. Although random forest (RF) has been successfully applied to streamflow ...

Understanding information as well as its redundancy, or duplication, has been crucial in the development of many of our everyday items such mobile phones, the internet, the compact disc as well as ...

In the era of big data, there are increasing interests on clustering variables for the minimization of data redundancy and the maximization of variable relevancy. Existing clustering methods, however, ...

This diagram depicts the decomposition of the various types of causality according to the SURD method: unique (pink), synergistic (orange), and redundant (dark grey) for two observed variables, to ...