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SMOTEBoost: improving prediction of the minority class in boosting (2003)
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| Venue: | In Proceedings of the Principles of Knowledge Discovery in Databases, PKDD-2003 |
| Citations: | 121 - 13 self |
BibTeX
@INPROCEEDINGS{Chawla03smoteboost:improving,
author = {Nitesh V. Chawla and Ar Lazarevic and Lawrence O. Hall and Kevin W. Bowyer},
title = {SMOTEBoost: improving prediction of the minority class in boosting},
booktitle = {In Proceedings of the Principles of Knowledge Discovery in Databases, PKDD-2003},
year = {2003},
pages = {107--119}
}
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Abstract
Abstract. Many real world data mining applications involve learning from imbalanced data sets, where the particular events of interest may be very few when compared to the other classes. Learning from data sets that contain rare events usually produces biased classifiers that have a higher predictive accuracy over the majority class(es), but poorer predictive accuracy over the minority class of interest. SMOTE (Synthetic Minority Over-sampling Technique) is a recent approach that is specifically designed for learning with minority classes. Boosting is a promising ensemble-based learning algorithm that can improve the classification performance of any weak classifier. This paper presents a novel approach for learning from rare classes, based on a combination of the SMOTE algorithm and the boosting procedure. Unlike standard boosting where all misclassified examples are given equal weights, the novel SMOTEBoost approach creates synthetic examples from the minority class, thus indirectly changing the updating weights and compensating for skewed distributions. The SMOTEBoost algorithm applied to several highly and moderately imbalanced data sets shows improvement in prediction performance on the rare class compared to standard boosting and to the SMOTE algorithm used with a single classifier. 1.
Keyphrases
minority class data set predictive accuracy rare class smote algorithm synthetic example classification performance rare event novel approach single classifier novel smoteboost approach biased classifier updating weight majority class synthetic minority over-sampling technique weak classifier standard boosting recent approach particular event imbalanced data set smoteboost algorithm prediction performance promising ensemble-based learning boosting procedure equal weight skewed distribution
