An Active Learning Approach with Uncertainty, Representativeness, and Diversity
Big data from the Internet of Things may create big challenge for data classification. Most active learning approaches select either uncertain or representative unlabeled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for q...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/827586 |
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| _version_ | 1832560264027832320 |
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| author | Tianxu He Shukui Zhang Jie Xin Pengpeng Zhao Jian Wu Xuefeng Xian Chunhua Li Zhiming Cui |
| author_facet | Tianxu He Shukui Zhang Jie Xin Pengpeng Zhao Jian Wu Xuefeng Xian Chunhua Li Zhiming Cui |
| author_sort | Tianxu He |
| collection | DOAJ |
| description | Big data from the Internet of Things may create big challenge for data classification. Most active learning approaches select either uncertain or representative unlabeled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for query selection, they are usually ad hoc in finding unlabeled instances that are both informative and representative and fail to take the diversity of instances into account. We address this challenge by presenting a new active learning framework which considers uncertainty, representativeness, and diversity creation. The proposed approach provides a systematic way for measuring and combining the uncertainty, representativeness, and diversity of an instance. Firstly, use instances’ uncertainty and representativeness to constitute the most informative set. Then, use the kernel k-means clustering algorithm to filter the redundant samples and the resulting samples are queried for labels. Extensive experimental results show that the proposed approach outperforms several state-of-the-art active learning approaches. |
| format | Article |
| id | doaj-art-9f85c8d4d24047e192b94945d62010f4 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-9f85c8d4d24047e192b94945d62010f42025-02-03T01:28:04ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/827586827586An Active Learning Approach with Uncertainty, Representativeness, and DiversityTianxu He0Shukui Zhang1Jie Xin2Pengpeng Zhao3Jian Wu4Xuefeng Xian5Chunhua Li6Zhiming Cui7School of Computer Science and Technology, Soochow University, Suzhou 215006, ChinaSchool of Computer Science and Technology, Soochow University, Suzhou 215006, ChinaSchool of Computer Science and Technology, Soochow University, Suzhou 215006, ChinaSchool of Computer Science and Technology, Soochow University, Suzhou 215006, ChinaSchool of Computer Science and Technology, Soochow University, Suzhou 215006, ChinaSchool of Computer Science and Technology, Soochow University, Suzhou 215006, ChinaSchool of Computer Science and Technology, Soochow University, Suzhou 215006, ChinaSchool of Computer Science and Technology, Soochow University, Suzhou 215006, ChinaBig data from the Internet of Things may create big challenge for data classification. Most active learning approaches select either uncertain or representative unlabeled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for query selection, they are usually ad hoc in finding unlabeled instances that are both informative and representative and fail to take the diversity of instances into account. We address this challenge by presenting a new active learning framework which considers uncertainty, representativeness, and diversity creation. The proposed approach provides a systematic way for measuring and combining the uncertainty, representativeness, and diversity of an instance. Firstly, use instances’ uncertainty and representativeness to constitute the most informative set. Then, use the kernel k-means clustering algorithm to filter the redundant samples and the resulting samples are queried for labels. Extensive experimental results show that the proposed approach outperforms several state-of-the-art active learning approaches.http://dx.doi.org/10.1155/2014/827586 |
| spellingShingle | Tianxu He Shukui Zhang Jie Xin Pengpeng Zhao Jian Wu Xuefeng Xian Chunhua Li Zhiming Cui An Active Learning Approach with Uncertainty, Representativeness, and Diversity The Scientific World Journal |
| title | An Active Learning Approach with Uncertainty, Representativeness, and Diversity |
| title_full | An Active Learning Approach with Uncertainty, Representativeness, and Diversity |
| title_fullStr | An Active Learning Approach with Uncertainty, Representativeness, and Diversity |
| title_full_unstemmed | An Active Learning Approach with Uncertainty, Representativeness, and Diversity |
| title_short | An Active Learning Approach with Uncertainty, Representativeness, and Diversity |
| title_sort | active learning approach with uncertainty representativeness and diversity |
| url | http://dx.doi.org/10.1155/2014/827586 |
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