基于流形排序的动态过抽样方法研究
发布时间：2019-01-24 07:51
【摘要】：针对传统过抽样容易出现数据冗余和局限于处理静态数据的问题,提出一种基于流形排序的动态过抽样方法。该方法采用流形结构描述数据,根据数据内在的全局流形结构对少数类数据进行排序,选择出排序值高的数据执行重采样策略,以达到改善数据平衡度的目的。实验结果表明,在动态的不平衡数据集上,该方法获得了比当前同类方法更好的分类效果,还能有效提升分类器对少数类的识别性能。
[Abstract]:A dynamic oversampling method based on manifold sorting is proposed to solve the problem that data redundancy is easy to occur in traditional oversampling and it is limited to deal with static data. The method uses manifold structure to describe the data, sorts a few kinds of data according to the global manifold structure of the data, and selects the data with high sorting value to carry out resampling strategy, so as to improve the data balance. The experimental results show that the proposed method has better classification performance than the current similar methods on dynamic unbalanced datasets and can effectively improve the recognition performance of the classifier for a few classes.
【作者单位】： 东北电力大学信息工程学院;吉林供电公司信息通信分公司;
【分类号】：O212.2
，
本文编号：2414264
[Abstract]:A dynamic oversampling method based on manifold sorting is proposed to solve the problem that data redundancy is easy to occur in traditional oversampling and it is limited to deal with static data. The method uses manifold structure to describe the data, sorts a few kinds of data according to the global manifold structure of the data, and selects the data with high sorting value to carry out resampling strategy, so as to improve the data balance. The experimental results show that the proposed method has better classification performance than the current similar methods on dynamic unbalanced datasets and can effectively improve the recognition performance of the classifier for a few classes.
【作者单位】： 东北电力大学信息工程学院;吉林供电公司信息通信分公司;
【分类号】：O212.2
，
本文编号：2414264
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