...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Future generation computer systems >A novel sub-Kmeans based on co-training approach by transforming single-view into multi-view
【24h】

A novel sub-Kmeans based on co-training approach by transforming single-view into multi-view

机译：基于共同训练方法的新型次媒体，通过将单视图转换为多视图

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

Semi-supervised learning is dedicated to solving the problem of poor model performance caused by the scarcity of labeled samples. Co-training algorithm, as a representative of semi-supervised learning algorithms, through constructing the diversity of classifiers, is used to solve the shortage of label samples that leads to low classifier accuracy. However, traditional Co-training style algorithms have strong constraints: (1) there are multiple views of data, and (2) the randomness of data views is relatively large, which leads to the low stability of the Co-training style algorithms. To optimize and solve the shortcomings of traditional Co-training style algorithms, a novel Co-training method based on sub-Kmeans named Op-FSCO is proposed. The proposed approach uses an optimal subspace construct method to extend Co-training to single-view data, greatly improving the application field of Co-training style algorithm. The clustering subspace is generated by plotting the probability of the correlation between the feature and the mutual information measure between the feature and the class label. For the resulting cluster subspace, the top two are selected to construct different views. Finally, experiments on a synthetic dataset, UCI dataset, and real-world dataset demonstrate that the proposed approach is more effective than other Co-training style algorithms. On the UCI benchmark dataset, the performance of the Op-FSCO algorithm on 15 UCI datasets is significantly better than the traditional Co-training algorithm. And on the cere bral stroke dataset, our approach is 2%-5% higher than that of traditional Co-training style algorithms.
机译：半监督学习致力于解决由标记样品稀缺引起的模型性能差的问题。通过构建分类器的多样性，通过构建分类器的多样性来解决与分类器的多样性的共同训练算法。然而，传统的共同训练样式算法具有强大的约束：（1）数据的多种视图，（2）数据视图的随机性相对较大，这导致了共同训练样式算法的低稳定性。为了优化和解决传统共同训练风格算法的缺点，提出了一种基于名为OP-FSCO的子浏览器的新型共同训练方法。所提出的方法使用最佳子空间构造方法来扩展到单视图数据的共同培训，大大改善了共同训练样式算法的应用领域。通过绘制特征与特征与类标签之间的相互信息测量之间的相关性的概率来生成聚类子空间。对于生成的群集子空间，选中前两个以构造不同的视图。最后，在合成数据集，UCI数据集和现实世界数据集上的实验表明，所提出的方法比其他共同训练样式算法更有效。在UCI基准数据集上，OP-FSCO算法在15个UCI数据集中的性能显着优于传统的共同训练算法。在Cere Bral Stroke DataSet上，我们的方法比传统的共同训练风格算法高2％-5％。

著录项

  • 来源
    《Future generation computer systems》 |2021年第12期|831-843|共13页
  • 作者

    Fengtao Nan; Yahui Tang; Po Yang; Zhenli He; Yun Yang;

  • 作者单位

    School of Information Science and Engineering Yunnan University Kunming 650500 China;

    School of Information Science and Engineering Yunnan University Kunming 650500 China;

    Department of Computer Science The University of Sheffield Sheffield United Kingdom;

    National Pilot School of Software Yunnan University Kunming 650500 China;

    National Pilot School of Software Yunnan University Kunming 650500 China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Co-training; Cluster subspace; Feature subspace;

    机译：共同培训;集群子空间;特征子空间;

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