Semi-supervised Classification Based Mixed Sampling for Imbalanced Data
In practical application, there are a large amount of imbalanced data containing only a small number of labeled data. In order to improve the classification performance of this kind of problem, this paper proposes a semi-supervised learning algorithm based on mixed sampling for imbalanced data class...
Main Authors: | Zhao Jianhua, Liu Ning |
---|---|
Format: | Article |
Language: | English |
Published: |
De Gruyter
2019-12-01
|
Series: | Open Physics |
Subjects: | |
Online Access: | https://doi.org/10.1515/phys-2019-0103 |
Similar Items
-
An activity window model for social interaction structure on Twitter
by: Zhang Jun, et al.
Published: (2018-11-01) -
A Big Data Analysis Method Based on Modified Collaborative Filtering Recommendation Algorithms
by: Yin Nan
Published: (2019-12-01) -
Research on the method of information system risk state estimation based on clustering particle filter
by: Cui Jia, et al.
Published: (2017-05-01) -
Entropy-based approach to missing-links prediction
by: Federica Parisi, et al.
Published: (2018-07-01) -
Comparisons of feature extraction algorithm based on unmanned aerial vehicle image
by: Xi Wenfei, et al.
Published: (2017-07-01)