Classification of imbalanced data
Imbalance datasets exist in many real-world domains. It is straightforward to apply classification algorithms when the dataset is balanced. However, when there is imbalanced dataset and the objective is to detect a rare but important class/case, either modifications to the prevailing classification...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/10356/75327 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Imbalance datasets exist in many real-world domains. It is straightforward to apply classification algorithms when the dataset is balanced. However, when there is imbalanced dataset and the objective is to detect a rare but important class/case, either modifications to the prevailing classification algorithms or dataset rebalancing are required. The objective here is to study different classification algorithms and dataset rebalancing mechanisms that can handle imbalance datasets effectively. The student is required to choose some popular classifiers and investigate how these classifiers can be altered to better handle the imbalanced datasets. |
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