Multi-class Pattern Classification in Imbalanced Data
The majority of multi-class pattern classification techniques are proposed for learning from balanced datasets. However, in several real-world domains, the datasets have imbalanced data distribution, where some classes of data may have few training examples compared for other classes. In this paper...
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| Format: | Conference Paper |
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IEEE
2010
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| Online Access: | http://hdl.handle.net/20.500.11937/24624 |
| _version_ | 1848751482111787008 |
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| author | Ghanem, Amal Venkatesh, Svetha West, Geoffrey |
| author2 | - |
| author_facet | - Ghanem, Amal Venkatesh, Svetha West, Geoffrey |
| author_sort | Ghanem, Amal |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The majority of multi-class pattern classification techniques are proposed for learning from balanced datasets. However, in several real-world domains, the datasets have imbalanced data distribution, where some classes of data may have few training examples compared for other classes. In this paper we present our research in learning from imbalanced multi-class data and propose a new approach, named Multi-IM, to deal with this problem. Multi-IM derives its fundamentals from the probabilistic relational technique (PRMs-IM), designed for learning from imbalanced relational data for the two-class problem. Multi-IM extends PRMs-IM to a generalized framework for multi-class imbalanced learning for both relational and non-relational domains. |
| first_indexed | 2025-11-14T07:53:25Z |
| format | Conference Paper |
| id | curtin-20.500.11937-24624 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:53:25Z |
| publishDate | 2010 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-246242017-09-13T15:55:52Z Multi-class Pattern Classification in Imbalanced Data Ghanem, Amal Venkatesh, Svetha West, Geoffrey - imbalanced class problem ensemble learning multi-class classification The majority of multi-class pattern classification techniques are proposed for learning from balanced datasets. However, in several real-world domains, the datasets have imbalanced data distribution, where some classes of data may have few training examples compared for other classes. In this paper we present our research in learning from imbalanced multi-class data and propose a new approach, named Multi-IM, to deal with this problem. Multi-IM derives its fundamentals from the probabilistic relational technique (PRMs-IM), designed for learning from imbalanced relational data for the two-class problem. Multi-IM extends PRMs-IM to a generalized framework for multi-class imbalanced learning for both relational and non-relational domains. 2010 Conference Paper http://hdl.handle.net/20.500.11937/24624 10.1109/ICPR.2010.706 IEEE fulltext |
| spellingShingle | imbalanced class problem ensemble learning multi-class classification Ghanem, Amal Venkatesh, Svetha West, Geoffrey Multi-class Pattern Classification in Imbalanced Data |
| title | Multi-class Pattern Classification in Imbalanced Data |
| title_full | Multi-class Pattern Classification in Imbalanced Data |
| title_fullStr | Multi-class Pattern Classification in Imbalanced Data |
| title_full_unstemmed | Multi-class Pattern Classification in Imbalanced Data |
| title_short | Multi-class Pattern Classification in Imbalanced Data |
| title_sort | multi-class pattern classification in imbalanced data |
| topic | imbalanced class problem ensemble learning multi-class classification |
| url | http://hdl.handle.net/20.500.11937/24624 |