Attribute Reduction Algorithm Research Based on Rough Core and Back Elimination

Machine learning algorithms are known to degrade in performance when facing with many features that are not necessary in the field of artificial intelligence and pattern recognition. Rough set theory is a new effective tool in dealing with vagueness and uncertainty information. Attribute reduction is one of the most important concepts in rough set theory and application research. Once it gets the whole reduction set, the reasoning capability with multi attributes absent can behave well. There are few research papers on whole reduction set of knowledge system. Thus how to get the whole reduction set is worth a problem to research. In this paper, we first get the rough core, then calculate superset of rough reduction, finally get the whole really rough reduction set with back elimination. Experiment shows that the attribute reduction algorithm based on core and back elimination is effective.