What is it about?
We select a best subset of features using information theory, especially the notion of complementarity where a subset of features is worth more than the the individual features in the subset. We minimize redundancy, maximize complementarity, while keeping the subset of features as small as possible.
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Why is it important?
Our feature subset selection helps build better classifiers regardless of the method used for classification.
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This page is a summary of: An adaptive heuristic for feature selection based on complementarity, Machine Learning, June 2018, Springer Science + Business Media,
DOI: 10.1007/s10994-018-5728-y.
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