Systems Engineering and Electronics

• SOFTWARE ALGORITHM AND SIMULATION • Previous Articles     Next Articles

Unsupervised feature selection based on Markov blanket and particle swarm optimization

Yintong Wang1,2,*, Jiandong Wang1, Hao Liao3, and Haiyan Chen1   

  1. 1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
    2. Key Laboratory of Trusted Cloud Computing and Big Data Analysis, Nanjing Xiaozhuang University, Nanjing 211171, China;
    3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
  • Online:2017-02-24 Published:2010-01-03


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.