Aiming at the problem that most algorithms of granular computing ignore the class-imbalance of data， a feature selection algorithm integrating pseudo-label strategy was proposed to deal with class-imbalanced data. Firstly， to investigate feature selection from class-imbalanced data conveniently， the sample consistency and dataset consistency were re-defined， and the corresponding greedy forward search algorithm for feature selection was designed. Then， the pseudo-label strategy was introduced to balance the class distribution of the data. By integrating the learned pseudo-label of a sample into consistency measure， the pseudo-label consistency was defined to estimate the features of the class-imbalanced dataset. Finally， an algorithm for Pseudo-Label Consistency based Feature Selection （PLCFS） for class-imbalanced data was developed based on the preservation of the pseudo-label consistency measure for the class-imbalanced dataset. Experimental results indicate that the proposed PLCFS has the performance only lower than max-Relevancy and Min-Redundancy （mRMR） algorithm， but outperforms Relief algorithm and algorithm for Consistency-based Feature Selection （CFS）.

In order to solve the problem of trajectory privacy leakage caused by the collection of numerous trajectory information of moving objects， a dummy trajectory-based trajectory privacy protection algorithm was proposed. In this algorithm， considering the user’s locations under disclosure， a heuristic rule was designed based on the comprehensive measure of trajectory similarity and location diversity to select the dummy trajectories， so that the generated dummy trajectories were able to effectively hide the real trajectory and sensitive locations. Besides， the trajectory directed graph strategy and the grid-based map strategy were proposed to optimize the execution efficiency of the algorithm. Experimental results on real trajectory datasets demonstrate that the proposed algorithm can effectively protect the real trajectory with high data utility.