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Feature weighting algorithms try to solve a problem of great importancenowadays in machine learning: The search of a relevance measure for thefeatures of a given domain. This relevance is primarily used for featureselection as feature weighting can be seen as a generalization of it, but it isalso useful to better understand a problem's domain or to guide an inductor inits learning process. Relief family of algorithms are proven to be veryeffective in this task. Some other feature weighting methods are reviewed inorder to give some context and then the different existing extensions to theoriginal algorithm are explained.  One of Relief's known issues is the performance degradation of its estimateswhen redundant features are present. A novel theoretical definition ofredundancy level is given in order to guide the work towards an extension ofthe algorithm that is more robust against redundancy. A new extension ispresented that aims for improving the algorithms performance. Some experimentswere driven to test this new extension against the existing ones with a set ofartificial and real datasets and denoted that in certain cases it improves theweight's estimation accuracy.

Toward better feature weighting algorithms: a focus on Relief