Abstract
Synthetic minority over-sampling technique (SMOTE) is an effective over-sampling technique and specifically designed for learning from imbalanced data sets. However, in the process of synthetic sample generation, SMOTE is of some blindness. This paper proposes a novel approach for imbalanced problem, based on a combination of the Threshold SMOTE (TSMOTE) and the Attribute Bagging (AB) algorithms. TSMOTE takes full advantage of majority samples to adjust the neighbor selective strategy of SMOTE in order to control the quality of the new sample. Attribute Bagging, a famous ensemble learning algorithm, is also used to improve the predictive power of the classifier. A comprehensive suite of experiments tested on 7 imbalanced data sets collected from UCI machine learning repository is conducted. Experimental results show that TSMOTE-AB outperforms the SMOTE and other previously known algorithms.
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Wang, J., Yun, B., Huang, P., Liu, YA. (2013). Applying Threshold SMOTE Algoritwith Attribute Bagging to Imbalanced Datasets. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds) Rough Sets and Knowledge Technology. RSKT 2013. Lecture Notes in Computer Science(), vol 8171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41299-8_21
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DOI: https://doi.org/10.1007/978-3-642-41299-8_21
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