Support-vector-based iteratively adjusted centroid classifier for text categorization

被引：0

作者：

Wang, Deqing ^{[1
]}

Zhang, Hui ^{[1
]}

机构：

[1] State Key Laboratory of Software Development Environment, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

来源：

Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2013年 / 39卷 / 02期

关键词：

Text processing - Vectors - Iterative methods;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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.

引用

页码：269 / 274