A New Population Initialization of Particle Swarm Optimization Method Based on PCA for Feature Selection

In many fields such as signal processing, machine learning, pattern recognition and data mining, it is common practice to process datasets containing huge numbers of features. cases, Feature Selection (FS) often involved. Meanwhile, owing their excellent global search ability, evolutionary computation techniques have been widely employed the FS. So, a powerful method calculation fast than other EC algorithms, PSO can solve features selection problems well. However, when facing large number feature selection, efficiency drops significantly. Therefore, plenty works done improve this situation. Besides, studies shown that an appropriate population initialization effectively help problem. basing on PSO, paper introduces new with filter-based population. The proposed algorithm uses Principal Component Analysis (PCA) measure importance first, then based sorted information, using threshold mixed proposed. experiments were performed several compared related algorithms. Experimental results show accuracy significantly improved after method.