On the basis of analyzing the rough set theory and immune computational theory

a hybrid attribute reduction algorithm is proposed in this paper.In order to enhance the diversity and stability of the antibodies group

the attribute kernel parameter is taken into antibodies coding.Then the population is vaccinated stochastically through a bacterin extraction algorithm.The approximate quality is taken as the affinity function objection

which is in order to reduce computational complexity of this algorithm.A niche immune sharing mechanism is introduced in the optimization process

which can dynamically adjust the affinity of antibodies and improve the local searching ability of the population.Through all these operators to prompt the convergence speed and kept the balance of global and local optimization.The experiments of the rolling bearing fault diagnosis and UCI data sets reduction have illustrated that the algorithm has outstanding advantages in precision and efficiency.