  Feature selection plays a critical role in biomedical data mining, driven by
increasing feature dimensionality in target problems and growing interest in
advanced but computationally expensive methodologies able to model complex
associations. Specifically, there is a need for feature selection methods that
are computationally efficient, yet sensitive to complex patterns of
association, e.g. interactions, so that informative features are not mistakenly
eliminated prior to downstream modeling. This paper focuses on Relief-based
algorithms (RBAs), a unique family of filter-style feature selection algorithms
that have gained appeal by striking an effective balance between these
objectives while flexibly adapting to various data characteristics, e.g.
classification vs. regression. First, this work broadly examines types of
feature selection and defines RBAs within that context. Next, we introduce the
original Relief algorithm and associated concepts, emphasizing the intuition
behind how it works, how feature weights generated by the algorithm can be
interpreted, and why it is sensitive to feature interactions without evaluating
combinations of features. Lastly, we include an expansive review of RBA
methodological research beyond Relief and its popular descendant, ReliefF. In
particular, we characterize branches of RBA research, and provide comparative
summaries of RBA algorithms including contributions, strategies, functionality,
time complexity, adaptation to key data characteristics, and software
availability.
