Semi-supervised Gaussian process classification algorithm addressing the class imbalance
The traditional supervised learning is difficult to deal with real-world datasets with less labeled information when the training sets class is imbalanced.Therefore,a new semi-supervised Gaussian process classification of address-ing was proposed.The semi-supervised Gaussian process was realized by...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2013-05-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2013.05.005/ |
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| _version_ | 1841539830440263680 |
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| author | Zhan-guo XIA Shi-xiong XIA Shi-yu CAI Ling WAN |
| author_facet | Zhan-guo XIA Shi-xiong XIA Shi-yu CAI Ling WAN |
| author_sort | Zhan-guo XIA |
| collection | DOAJ |
| description | The traditional supervised learning is difficult to deal with real-world datasets with less labeled information when the training sets class is imbalanced.Therefore,a new semi-supervised Gaussian process classification of address-ing was proposed.The semi-supervised Gaussian process was realized by calculating the posterior probability to obtain more accurate and credible labeled data,and embarking from self-training semi-supervised methods to add class label into the unlabeled data.The algorithm makes the distribution of training samples relatively balance,so the classifier can adaptively optimized to obtain better effect of classification.According to the experimental results,when the circum-stances of training set are class imbalance and much lack of label information,The algorithm improves the accuracy by obtaining effective labeled in comparison with other related works and provides a new idea for addressing the class im-balance is demonstrated. |
| format | Article |
| id | doaj-art-ad23b4b4c7fa45538ebdc77c2d3e2fe0 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2013-05-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-ad23b4b4c7fa45538ebdc77c2d3e2fe02025-01-14T06:35:13ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2013-05-0134425159671938Semi-supervised Gaussian process classification algorithm addressing the class imbalanceZhan-guo XIAShi-xiong XIAShi-yu CAILing WANThe traditional supervised learning is difficult to deal with real-world datasets with less labeled information when the training sets class is imbalanced.Therefore,a new semi-supervised Gaussian process classification of address-ing was proposed.The semi-supervised Gaussian process was realized by calculating the posterior probability to obtain more accurate and credible labeled data,and embarking from self-training semi-supervised methods to add class label into the unlabeled data.The algorithm makes the distribution of training samples relatively balance,so the classifier can adaptively optimized to obtain better effect of classification.According to the experimental results,when the circum-stances of training set are class imbalance and much lack of label information,The algorithm improves the accuracy by obtaining effective labeled in comparison with other related works and provides a new idea for addressing the class im-balance is demonstrated.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2013.05.005/class imbalancesemi-supervisedGaussian process classificationself-training |
| spellingShingle | Zhan-guo XIA Shi-xiong XIA Shi-yu CAI Ling WAN Semi-supervised Gaussian process classification algorithm addressing the class imbalance Tongxin xuebao class imbalance semi-supervised Gaussian process classification self-training |
| title | Semi-supervised Gaussian process classification algorithm addressing the class imbalance |
| title_full | Semi-supervised Gaussian process classification algorithm addressing the class imbalance |
| title_fullStr | Semi-supervised Gaussian process classification algorithm addressing the class imbalance |
| title_full_unstemmed | Semi-supervised Gaussian process classification algorithm addressing the class imbalance |
| title_short | Semi-supervised Gaussian process classification algorithm addressing the class imbalance |
| title_sort | semi supervised gaussian process classification algorithm addressing the class imbalance |
| topic | class imbalance semi-supervised Gaussian process classification self-training |
| url | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2013.05.005/ |
| work_keys_str_mv | AT zhanguoxia semisupervisedgaussianprocessclassificationalgorithmaddressingtheclassimbalance AT shixiongxia semisupervisedgaussianprocessclassificationalgorithmaddressingtheclassimbalance AT shiyucai semisupervisedgaussianprocessclassificationalgorithmaddressingtheclassimbalance AT lingwan semisupervisedgaussianprocessclassificationalgorithmaddressingtheclassimbalance |