Abstract: An incremental attribute reduction algorithm for complex information systems with discrete and continu- ous mixed attributes was proposed． Firstly，the knowledge granulation in the granular computing model was extend- ed under the mixed information system，the neighborhood knowledge granulation was proposed，and a non-incre- mental attribute reduction algorithm based on the neighborhood knowledge granulation was constructed． Then，the incremental computation of neighborhood knowledge granulation with the increase of objects was studied under the mixed information system，and the efficiency of the computation was proved theoretically． Finally，an incremental attribute reduction algorithm for mixed information systems based on neighborhood knowledge granulation was pro- posed． Experimental results of UCI datasets show that the proposed algorithm has high incremental attribute reduc- tion performance in mixed information systems．

Key words: complex information system, mixed attributes, attribute reduction, knowledge granulation, neighbor- hood, incremental learning