• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mathematical Problems in Engineering >Efficient Optimization of F-Measure with Cost-Sensitive SVM
【24h】

Efficient Optimization of F-Measure with Cost-Sensitive SVM

机译：使用成本敏感型SVM的F-measure的有效优化

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

F-measure is one of the most commonly used performance metrics in classification, particularly when the classes are highly imbalanced. Direct optimization of this measure is often challenging, since no closed form solution exists. Current algorithms design the classifiers by using the approximations to the F-measure. These algorithms are not efficient and do not scale well to the large datasets. To fill the gap, in this paper, we propose a novel algorithm, which can efficiently optimize F-measure with cost-sensitive SVM. First of all, we present an explicit transformation from the optimization of F-measure to cost-sensitive SVM. Then we adopt bundle method to solve the inner optimization. For the problem where the existing bundle method may have the fluctuations in the primal objective during iterations, an additional line search procedure is involved, which can alleviate the fluctuations problem and make our algorithm more efficient. Empirical studies on the large-scale datasets demonstrate that our algorithm can provide significant speedups over current state-of-the-art F-measure based learners, while obtaining better (or comparable) precise solutions.
机译：F度量是分类中最常用的性能指标之一，尤其是在类别高度不平衡的情况下。由于不存在封闭形式的解决方案，因此直接优化此方法通常具有挑战性。当前的算法通过使用F度量的近似值来设计分类器。这些算法效率不高，无法很好地扩展到大型数据集。为了弥补这一空白，本文提出了一种新颖的算法，该算法可以使用成本敏感型SVM有效地优化F测度。首先，我们提出了从F度量优化到成本敏感型SVM的明确转换。然后采用捆绑法求解内部优化问题。对于现有的bundle方法在迭代过程中原始目标可能存在波动的问题，需要使用额外的行搜索过程，这可以缓解波动问题并提高我们的算法的效率。对大规模数据集的经验研究表明，我们的算法可以大大提高当前基于最新F度量的学习者的学习速度，同时可以获得更好（或类似）的精确解。

著录项

  • 来源
    《Mathematical Problems in Engineering》 |2016年第5期|5873769.1-5873769.11|共11页
  • 作者

    Cheng Fan; Zhou Yuan; Gao Jian; Zheng Shuangqiu;

  • 作者单位

    Anhui Univ, Minist Educ, Key Lab Intelligent Comp & Signal Proc, 3 Feixi Rd, Hefei 230039, Anhui, Peoples R China|Anhui Univ, Sch Comp, 3 Feixi Rd, Hefei 230039, Peoples R China;

    Anhui Univ, Sch Comp, 3 Feixi Rd, Hefei 230039, Peoples R China;

    Anhui Univ, Sch Comp, 3 Feixi Rd, Hefei 230039, Peoples R China;

    Anhui Univ, Sch Comp, 3 Feixi Rd, Hefei 230039, Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号