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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Bibliographic Details
Main Authors: Ghanem, Amal S, Venkatesh, Svetha, West, Geoff
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
ensemble learning
Glass
imbalanced class problem
multi-class classification
Pattern recognition
Probabilistic logic
Testing
Training
Training data
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Summary: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.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2010.706