The ensemble learning technology often uses each basic classifier that has been trained to form a complete ensemble system， and the largeness of the ensemble system easily leads to more memory and time. So as to gain the high prediction correctness and low classification time of the ensemble classification model， according to the research of attribute reduction in rough sets， this paper proposes a self-sampling ensemble classification method based on the attribute reduction. This method applies the strategy of combining ant colony optimization and attribute reduction to the original feature data set， and then multiple optimal feature reduction subspaces are obtained. Taking any feature subset after reduction as the feature input of the integrated classifier can reduce the memory usage and classification time of the classifier to some extent. And then each self-sampling method taking the learning results and learning speed of the samples as constraints is combined to iteratively train each base classifier. Finally， the feasibility of the proposed method is further proved by experimental results.