Unsupervised feature selection based on Markov blanket and particle swarm optimization
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Yintong Wang, Key Laboratory of Trusted Cloud Computing and Big Data Analysis, Nanjing Xiaozhuang University, Jiandong Wang, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Hao Liao, College of Computer Science and Software Engineering, Shenzhen University, Haiyan Chen, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics · Journal of Systems Engineering and Electronics · 2017
Feature selection plays an important role in data mining and recognition, especially in the large scale text, image and biological data. Specifically, the class label information is unavailable to guide the selection of minimal feature subset in unsupervised feature selection, which is challenging and interesting. An unsupervised feature selection based on Markov blanket and particle swarm optimization is proposed named as UFSMB-PSO. The proposed method seeks to find the high-quality feature subset through multi-particles' cooperation of particle swarm optimization without using any learning algorithms. Moreover, the features' relevance will be computed based on an information metric of relevance gain, which provides an information theoretical foundation for finding the minimization of the redundancy between features. Our results on several benchmark datasets demonstrate that UFSMB-PSO can achieve significant improvement over state of the art unsupervised methods.