Predicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting
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Predicting rare classes: can boosting make any weak learner strong?
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Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Boosted Classification Trees and Class Probability/Quantile Estimation
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Cost-sensitive boosting for classification of imbalanced data
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ACM SIGKDD Explorations Newsletter
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Classification of rare vents has many important data mining applications. Boosting is a promising meta-techniquethat improves the classification performance of any weak classifier. So far, no systematic study has been conducted to evaluate how boosting performs for the task of mining rare classes. In this paper, we evaluate three existing categories of boosting algorithms from the single viewpoint of how they update the example weights in eachiteration, and discuss their possible effect on recall andprecision of the rare class. We propose enhanced algorithms in two of the categories, and justify their choice of weightupdating parameters theoretically. Using some specially designed synthetic datasets, we compare the capability of all the algorithms from the rare class perspective. Theresults support our qualitative analysis, and also indicate that our enhancements bring an extra capability for achieving better balance between recall and precision in mining rareclasses.