Interleaving Forward Backward Feature Selection
نویسندگان
چکیده
Selecting appropriate features has become a key task when dealing with high-dimensional data. We present a new algorithm designed to find an optimal solution for classification tasks. Our approach combines forward selection, backward elimination and exhaustive search. We demonstrate its capabilities and limits using artificial and real world data sets. Regarding artificial data sets interleaving forward backward selection performs similar as other well known feature selection methods.
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