A New Algorithm for Knowledge Reduction Based on Neighborhood Rough Set

In order to reduce the practical decision system including continuous attributes, a reduction algorithm based on neighborhood granulation is proposed. In this algorithm, a rough set model is used based on neighborhood equivalence, the indiscernibility relation is measured by neighborhood relation, and the universe spaces is approximated by neighborhood information granules. We construct a features selection algorithm of continuous attributes. The experimental results with UCI data set show that neighborhood model can select a few attributes but keep, even improve classification power. Some improvements for a widely used value reduction method are also achieved in this paper. Using this method reduce discrete information system, the complexity of acquired rule knowledge can be reduced effectively in this way.