Reduction of Rough Set Attribute Based on
Immune Clone Selection
Liang Lin1, Xu Guanghua2
(1.School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China;
2.State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University,
Xi'an 710049, China)
Abstract: A novel attribute reduction approach of rough set based on immune clone
selection was proposed. In this method, the approximation quality and attribute set were
used as evolution objection and antibody, respectively. On the basis of the inherent
distribution within the immune response, the global optimization of antibody was realized
through parallel local optimization. Moreover, the diversity of antibody population was
maintained with the affinity suppression and renewal of antibody. Thus the stable
multi-local optimal solutions can be preserved. In addition, the machinery fault data were
analyzed by this method, and the attribute reduction sets were obtained furthest to
satisfy the demand of feature selection in machinery diagnosis.
Keywords: immune clone selection; rough set; attribute reduction; fault diagnosis