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Volume 39 Issue 2
Feb.  2013
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Journal of Beijing University of Aeronautics and Astronautics  > 2013  >  39(2): 269-274.
ZHENG Lei, HU Weiduo, LIU Changet al. Large crater identification method based on deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(5): 994-1004. doi: 10.13700/j.bh.1001-5965.2019.0342(in Chinese)
Citation: Wang Deqing, Zhang Hui. Support-vector-based iteratively adjusted centroid classifier for text categorization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(2): 269-274. (in Chinese)
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Support-vector-based iteratively adjusted centroid classifier for text categorization

  • State Key Laboratory of Software Development Environment, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • Received Date: 11 Jan 2012
  • Publish Date: 28 Feb 2013
    Abstract
  • To address the lackness of centroid-based classifier (CC) that is prone to generate inductive bias or model misfit, a support-vector-based iteratively-adjusted centroid classifier (IACC_SV) was proposed, which employs support vectors found by some routines, e.g., linear support vector machines (SVMs) to construct centroid vectors for CC, and then iteratively adjusts the initial centroid vectors according to the misclassified training samples. Compared with traditional classification algorithms, IACC_SV achieves better performance in terms of macro-F1 and micro-F1, and the extensive experiments on 8 real-world text corpora demonstrate the effectiveness of the proposed algorithm, especially on text corpora with highly imbalanced classes.

     

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