Understanding a Version of Multivariate Symmetric Uncertainty to assist in Feature Selection
  In this paper, we analyze the behavior of the multivariate symmetric
uncertainty (MSU) measure through the use of statistical simulation techniques
under various mixes of informative and non-informative randomly generated
features. Experiments show how the number of attributes, their cardinalities,
and the sample size affect the MSU. We discovered a condition that preserves
good quality in the MSU under different combinations of these three factors,
providing a new useful criterion to help drive the process of dimension
reduction.
