On strategies for imbalanced text classification using SVM: A comparative study

Many real-world text classification tasks involve imbalanced training examples. The strategies proposed to address the imbalanced classification (e.g., resampling, instance weighting), however, have not been systematically evaluated in the text domain. In this paper, we conduct a comparative study o...

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Bibliographic Details
Published in:Decision Support Systems 2009-12, Vol.48 (1), p.191-201
Main Authors: Sun, Aixin, Lim, Ee-Peng, Liu, Ying
Format: Article
Language:English
Subjects:
Accuracy
Applied sciences
Artificial intelligence
Classification
Comparative analysis
Computer science
control theory
systems
Data processing. List processing. Character string processing
Decision theory. Utility theory
Exact sciences and technology
Imbalanced text classification
Instance weighting
Memory organisation. Data processing
Operational research and scientific management
Operational research. Management science
Resampling
Software
Speech and sound recognition and synthesis. Linguistics
Studies
Support Vector Machines
SVM
Taxonomy
Text categorization
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Summary:Many real-world text classification tasks involve imbalanced training examples. The strategies proposed to address the imbalanced classification (e.g., resampling, instance weighting), however, have not been systematically evaluated in the text domain. In this paper, we conduct a comparative study on the effectiveness of these strategies in the context of imbalanced text classification using Support Vector Machines (SVM) classifier. SVM is the interest in this study for its good classification accuracy reported in many text classification tasks. We propose a taxonomy to organize all proposed strategies following the training and the test phases in text classification tasks. Based on the taxonomy, we survey the methods proposed to address the imbalanced classification. Among them, 10 commonly-used methods were evaluated in our experiments on three benchmark datasets, i.e., Reuters-21578, 20-Newsgroups, and WebKB. Using the area under the Precision–Recall Curve as the performance measure, our experimental results showed that the best decision surface was often learned by the standard SVM, not coupled with any of the proposed strategies. We believe such a negative finding will benefit both researchers and application developers in the area by focusing more on thresholding strategies.
ISSN:0167-9236
1873-5797
DOI:10.1016/j.dss.2009.07.011