...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Expert systems with applications >NI-MWMOTE: An improving noise-immunity majority weighted minority oversampling technique for imbalanced classification problems
【24h】

NI-MWMOTE: An improving noise-immunity majority weighted minority oversampling technique for imbalanced classification problems

机译：NI-MWMOTE：一种提高抗噪性多数加权少数少数少数群体过采样技术，用于不平衡分类问题

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

Oversampling techniques have been favored by researchers because of their simplicity and versatility in dealing with imbalanced classification problems. For oversampling techniques appeared in recent years (e.g. Majority Weighted Minority Oversampling Technique (MWMOTE)), noise processing plays an important role. This is because the processing of noise directly affects the distribution of new synthetic instances. MWMOTE and many other oversampling techniques use knn based noise processing method. While the knn method can effectively handle partial noise when the neighborhood parameter k value is reasonable, it may lead to under-recognition or over-recognition without prior experience. Therefore, we propose an improving noise-immunity majority weighted minority oversampling technique abbreviated NI-MWMOTE. NI-MWMOTE uses an adaptive noise processing scheme, which combines Euclidean distance and neighbor density to rank the probability that suspected noise (knn method) is true noise, and then adaptively selects the best noise processing scheme through iteration and misclassification error. Then, aggregative hierarchical clustering (AHC) method is used to cluster minority instances. And, in each sub-cluster, the sampling size of new samples is adaptively determined by classification complexity and cross-validation. NI-MWMOTE not only avoids the generation of new noise, but also effectively overcomes both between-class imbalances and within-class imbalances. Results demonstrate that NI-MWMOTE achieves significantly better results in most imbalanced datasets than eight popular oversampling algorithms. (c) 2020 Elsevier Ltd. All rights reserved.
机译：在处理不平衡的分类问题时，研究人员已经受到过采样技术受到青睐。对于近年来出现的过采样技术（例如，大多数加权少数群体过采样技术（MWMOTE）），噪声处理起着重要作用。这是因为噪声的处理直接影响了新的合成实例的分布。 MWMOTE和许多其他过采样技术使用KNN噪声处理方法。虽然KNN方法可以在邻次参数k值合理时有效地处理部分噪声，但是在没有先前经验的情况下可能导致识别不足或过度识别。因此，我们提出了一种改善抗噪性多数加权少数群体过采样技术缩写了Ni-MwMote。 NI-MWMOTE使用自适应噪声处理方案，其结合了欧几里德距离和邻居浓度来对疑似噪声（KNN方法）的概率进行排名，然后通过迭代和错误分类错误自适应地选择最佳噪声处理方案。然后，聚合分层群集（AHC）方法用于纳入少数群体实例。并且，在每个子集群中，新样本的采样大小通过分类复杂性和交叉验证自适应地确定。 NI-MWMOTE不仅避免产生新的噪音，而且还有效地克服了阶级之间的不平衡和课堂内的不平衡。结果表明，NI-MWMMOTE在大多数不平衡的数据集中实现了比八个流行的过采样算法的显着更好的结果。 （c）2020 elestvier有限公司保留所有权利。

著录项

  • 来源
    《Expert systems with applications》 |2020年第11期|113504.1-113504.22|共22页
  • 作者

    Wei Jianan; Huang Haisong; Yao Liguo; Hu Yao; Fan Qingsong; Huang Dong;

  • 作者单位

    Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Guizhou Peoples R China;

    Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Guizhou Peoples R China;

    Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Guizhou Peoples R China|Yuan Ze Univ Dept Ind Engn & Management Taoyuan 32003 Taiwan;

    Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Guizhou Peoples R China|Guizhou Renhe Zhiyuan Data Serv Co Ltd Guiyang 50025 Guizhou Peoples R China;

    Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Guizhou Peoples R China;

    Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Guizhou Peoples R China;

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

    Imbalanced classification; Noise-immunity; MWMOTE; Clustering; Oversampling;

    机译：不平衡的分类;致致免疫;MWMOTE;聚类;过采样;

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