  Feature weighting algorithms try to solve a problem of great importance
nowadays in machine learning: The search of a relevance measure for the
features of a given domain. This relevance is primarily used for feature
selection as feature weighting can be seen as a generalization of it, but it is
also useful to better understand a problem's domain or to guide an inductor in
its learning process. Relief family of algorithms are proven to be very
effective in this task. Some other feature weighting methods are reviewed in
order to give some context and then the different existing extensions to the
original algorithm are explained.
  One of Relief's known issues is the performance degradation of its estimates
when redundant features are present. A novel theoretical definition of
redundancy level is given in order to guide the work towards an extension of
the algorithm that is more robust against redundancy. A new extension is
presented that aims for improving the algorithms performance. Some experiments
were driven to test this new extension against the existing ones with a set of
artificial and real datasets and denoted that in certain cases it improves the
weight's estimation accuracy.
