Abstract
The present work proposes a variant of the Minimal Learning Machine (MLM) in a cost sensitiVe framework for classification. MLM is a recently proposed supervised learning algorithm with a simple formulation and few hyperparameters. The proposed method is tested under two classification problems: imbalanced classification and classification with reject option. The results are comparable to other to state of the art classifiers.
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Acknowledgments
The authors acknowledge the support of CNPq (Grant 456837/2014-0).
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Gomes, J.P.P., Souza, A.H., Corona, F., Neto, A.R.R. (2015). A Cost Sensitive Minimal Learning Machine for Pattern Classification. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_61
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DOI: https://doi.org/10.1007/978-3-319-26532-2_61
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