  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.
  On previous work, a new extension was proposed that aimed for improving the
algorithm's performance and it was shown that in certain cases it improved the
weights' estimation accuracy. However, it also seemed to be sensible to some
characteristics of the data. An improvement of that previously presented
extension is presented in this work that aims to make it more robust to problem
specific characteristics. An experimental design is proposed to test its
performance. Results of the tests prove that it indeed increase the robustness
of the previously proposed extension.
