Abstract
We present a framework to address the imbalanced data problem using semi-supervised learning. Specifically, from a supervised problem, we create a semi-supervised problem and then use a semi-supervised learning method to identify the most relevant instances to establish a well-defined training set. We present extensive experimental results, which demonstrate that the proposed framework significantly outperforms all other sampling algorithms in 67% of the cases across three different classifiers and ranks second best for the remaining 33% of the cases.
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Almogahed, B.A., Kakadiaris, I.A. (2014). Empowering Imbalanced Data in Supervised Learning: A Semi-supervised Learning Approach. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_66
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DOI: https://doi.org/10.1007/978-3-319-11179-7_66
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