Managing Borderline and Noisy Examples in Imbalanced Classification by Combining SMOTE with Ensemble Filtering
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Editorial
Springer
Materia
Classification Imbalanced data SMOTE Class noise Noise filters
Date
2014Referencia bibliográfica
Published version: Sáez, J.A... [et al.] (2014). Managing Borderline and Noisy Examples in Imbalanced Classification by Combining SMOTE with Ensemble Filtering. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. [https://doi.org/10.1007/978-3-319-10840-7_8]
Abstract
Imbalance data constitutes a great difficulty for most algorithms
learning classifiers. However, as recent works claim, class imbalance
is not a problem in itself and performance degradation is also associated
with other factors related to the distribution of the data as the presence of
noisy and borderline examples in the areas surrounding class boundaries.
This contribution proposes to extend SMOTE with a noise filter called
Iterative-Partitioning Filter (IPF), which can overcome these problems.
The properties of this proposal are discussed in a controlled experimental
study against SMOTE and its most well-known generalizations. The
results show that the new proposal performs better than exiting SMOTE
generalizations for all these different scenarios.
Except where otherwise noted, this item's license is described as Atribución 4.0 Internacional