Feature selection is to choose a subset without containing redundant features， while keeping the classification performance of the data unchanged. Rough hypercuboid approaches can comprehensively evaluate the feature subsets from the three aspects of the relevance， dependency and significance of features， which have been used for feature selection successfully. However， calculating the combination of all feature subsets is NP-hard， and the results obtained by traditional forward search methods is locally optimal. Therefore， a new algorithm based on the rough hypercuboid approach is designed by integrating binary particle swarm optimization. The algorithm first introduces the feature relevance to generate a set of particles， then sets the improved objective function of the rough hypercuboid method as the optimization function， and finally finds the optimal feature subset by iterative optimization of binary particle swarm. By comparing with traditional rough hypercuboid methods and the rough set method based on particle swarm optimization， etc， experimental results demonstrate the proposed algorithm is able to acquire a feature subset with fewer features and higher classification performance.