为降低集成特征选择方法的计算复杂性，提出了一种基于粗糙集约简的神经网络集成分类方法。该方法首先通过结合遗传算法求约简和重采样技术的动态约简技术，获得稳定的、泛化能力较强的属性约简集;然后，基于不同约简设计BP网络作为待集成的基分类器，并依据选择性集成思想，通过一定的搜索策略，找到具有最佳泛化性能的集成网络;最后通过多数投票法实现神经网络集成分类。该方法在某地区Landsat 7波段遥感图像的分类实验中得到了验证，由于通过粗糙集约简，过滤掉了大量分类性能欠佳的特征子集，和传统的集成特征选择方法相比，该方法时

Neural network ensemble based on rough sets reduction is proposed to decrease the computing complexity of conventional feature ensemble selection algorithms. Firstly, a dynamic reduction technology, which integrates genetic algorithm and resample method, is used to get reduct sets that have stable and good generalization ability. Secondly, Multiple BP neural networks based on different reducts are built as base classifiers. According to the idea of selective ensemble, the best generalization ability neural network ensemble can be found by some search strategies. Finally, classification based on neural network ensemble can be completed by combination with vote rule. The method has been verified in the experiment of classifying Landsat 7 bands remote sensing image of chosen area. A number of feature sets of poor performance were discarded by reduction based on rough sets. Compared to conventional feature selection algorithms, the method takes less time, has lower computing complexity, and the performance is satisfying.