  Gene expression datasets are usually of high dimensionality and therefore
require efficient and effective methods for identifying the relative importance
of their attributes. Due to the huge size of the search space of the possible
solutions, the attribute subset evaluation feature selection methods tend to be
not applicable, so in these scenarios feature ranking methods are used. Most of
the feature ranking methods described in the literature are univariate methods,
so they do not detect interactions between factors. In this paper we propose
two new multivariate feature ranking methods based on pairwise correlation and
pairwise consistency, which we have applied in three gene expression
classification problems. We statistically prove that the proposed methods
outperform the state of the art feature ranking methods Clustering Variation,
Chi Squared, Correlation, Information Gain, ReliefF and Significance, as well
as feature selection methods of attribute subset evaluation based on
correlation and consistency with multi-objective evolutionary search strategy.
