Size Adaptive Selection of Most Informative Features


  • Si Liu Chinese Academy of Science
  • Hairong Liu National University of Singapore
  • Longin Jan Latecki Temple University
  • Shuicheng Yan National University of Singapore
  • Changsheng Xu China-Singapore Institute of Digital Media
  • Hanqing Lu Chinese Academy of Science


In this paper, we propose a novel method to select the most informativesubset of features, which has little redundancy andvery strong discriminating power. Our proposed approach automaticallydetermines the optimal number of features and selectsthe best subset accordingly by maximizing the averagepairwise informativeness, thus has obvious advantage overtraditional filter methods. By relaxing the essential combinatorialoptimization problem into the standard quadratic programmingproblem, the most informative feature subset canbe obtained efficiently, and a strategy to dynamically computethe redundancy between feature pairs further greatly acceleratesour method through avoiding unnecessary computationsof mutual information. As shown by the extensive experiments,the proposed method can successfully select the mostinformative subset of features, and the obtained classificationresults significantly outperform the state-of-the-art results onmost test datasets.




How to Cite

Liu, S., Liu, H., Latecki, L. J., Yan, S., Xu, C., & Lu, H. (2011). Size Adaptive Selection of Most Informative Features. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 392-397. Retrieved from



AAAI Technical Track: Machine Learning