Generalization ability of ensemble networks can be improved if the diversity of the individual network be increased. Consider this point, Rough_Boosting and Rough_Bagging are proposed as new individual network building algorithms. First, the training samples are disturbed by Boosting or Bagging methods, then, based on rough sets theory, proper attributes are selected by finding relative reducts. Thus, the mechanism of disturbing training data and the input attribute are combined to help generate accurate and diverse component networks. Experiment show that the generalization ability of proposed method obviously better than that of Boosting and Bagging methods, and individual networks generated have more diversity than that of Boosting or Bagging. Compared with prevailing similar methods, the proposed method has close or corresponsive performance.