Fast Learning and Testing for Imbalanced Multi-Class Changes in Streaming Data by Dynamic Multi-Stratum Network
Although several efficient learning methods have recently been proposed to handle class drift situations, issues remain in various streaming data applications that possibly deteriorate classification accuracy. Three important issues were considered, that is: 1) lifetime and class changes; 2) high im...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2017-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7945522/ |