Feature selection can improve the performance of data classification effectively. In order to further improve the solving ability of Ant Colony Optimization (ACO) on feature selection, a hybrid Ant colony optimization with Brain storm Optimization (ABO) algorithm was proposed. In the algorithm, the information communication archive was used to maintain the historical better solutions, and a longest time first method based on relaxation factor was adopted to update archive dynamically. When the global optimal solution of ACO was not updated for several times, a route-idea transformation operator based on Fuch chaotic map was used to transform the route solutions in the archive to the idea solutions. With the obtained solutions as initial population, the Brain Storm Optimization (BSO) was adopted to search for better solutions in wider space. On six typical binary datasets, experiments were conducted to analyze the sensibility of parameters of the proposed algorithm, and the algorithm was compared to three typical evolutionary algorithms:Hybrid Firefly and Particle Swarm Optimization (HFPSO) algorithm, Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA) and Genetic Algorithm (GA). Experimental results show that compared with the comparison algorithms, the proposed algorithm can improve the classification accuracy by at least 2.88% to 5.35%, and the F1-measure by at least 0.02 to 0.05, which verify the effectiveness and superiority of the proposed algorithm.

Aiming at the problems of limited monitoring area and difficult target locating in video security surveillance system, a radar-guided video linkage monitoring model was established with the characteristics of wide radar monitoring range and freedom from optical conditions. On this basis, a target location algorithm and a multi-target selection algorithmm were proposed. Firstly, according to the target information detected by radar, the corresponding camera azimuth and pitch angle of a moving target in the system linkage model were automatically calculated so that the target could be accurately locked, monitored and tracked by camera in real-time. Then, with multiple targets appearing in the surveillance scene, the multi-target selecting algorithm was used for data weighted fusion of discrete degree of target, radial velocity of target and the distance between target and camera to select the target with the highest priority for intensive monitoring. The experimental results show that, the locating accuracy of the proposed target location algorithm for pedestrians and vehicles can reach 0.94 and 0.84 respectively, which can achieve accurate target location. Moreover, the proposed multi-target selection algorithm can effectively select the best monitoring target in complex environment, and has good robustness and real-time performance.

A Robust Feature Selection and Classification algorithm based on Partial Least Squares Regression (RFSC-PLSR) was proposed to solve the problem of redundancy and multi-collinearity between features in feature selection. Firstly, the consistency coefficient of sample class based on neighborhood estimation was defined. Then, the k Nearest Neighbor ( kNN) operation was used to select the conservative samples with local class structure stability, and the partial least squares regression model was used to construct the robust feature selection. Finally, a partial least squares classification model was constructed using the class consistency coefficient and the preferred feature subset for all samples from a global structure perspective. Five data sets of different dimensions were selected from the UCI database for numerical experiments. The experimental results show that compared with four typical classifiers-Support Vector Machine (SVM), Naive Bayes (NB), Back-Propagation Neural Network (BPNN) and Logistic Regression (LR), RFSC-PLSR is more efficient in low-dimensional, medium-dimension, high-dimensional and other different cases, and shows stronger competitiveness in classification accuracy, robustness and computational efficiency.