Abstract
Prototype Selection methods aim at improving the efficiency of the Nearest Neighbour classifier by selecting a set of representative examples of the training set. These techniques have been studied in situations in which the classes at issue are balanced, which is not representative of real-world data. Since class imbalance affects the classification performance, data-level balancing approaches that artificially create or remove data from the set have been proposed. In this work, we study the performance of a set of prototype selection algorithms in imbalanced and algorithmically-balanced contexts using data-driven approaches. Results show that the initial class balance remarkably influences the overall performance of prototype selection, being generally the best performances found when data is algorithmically balanced before the selection stage.
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Acknowledgements
Work partially supported by the Spanish Ministerio de Economía y Competitividad through Project TIMuL (No. TIN2013-48152-C2-1-R supported by EU FEDER funds), the Spanish Ministerio de Educación, Cultura y Deporte through FPU program (AP2012–0939) and the Vicerrectorado de Investigación, Desarrollo e Innovación de la Universidad de Alicante through FPU program (UAFPU2014–5883).
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Valero-Mas, J.J., Calvo-Zaragoza, J., Rico-Juan, J.R., Iñesta, J.M. (2017). A Study of Prototype Selection Algorithms for Nearest Neighbour in Class-Imbalanced Problems. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_37
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DOI: https://doi.org/10.1007/978-3-319-58838-4_37
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