The Reduction Algorithm of Hybrid Decision System Based on Neighborhood Granulation and Niche Clone Selection

In order to reduce the hybrid decision system, a reduction algorithm is proposed based on the neighborhood rough set model and niche clone selection algorithm. In the model the indiscernibility relation is measured by neighborhood relation, and the universe spaces is approximated by neighborhood information granules, so the numerical attributes can be treated directly. The fitness function is designed, and the reduction algorithm is presented as well. The introduction of niche technology can avoid the early convergence of the antibody, and can avoid the sensitization and the local astringency of the parameter to the specific optimal objects. The validity and feasibility of the algorithm are demonstrated by the results of experiments on a classical data set and four UCI machine learning databases.