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Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition

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Abstract

The presence of noise in data is a common problem that produces several negative consequences in classification problems. In multi-class problems, these consequences are aggravated in terms of accuracy, building time, and complexity of the classifiers. In these cases, an interesting approach to reduce the effect of noise is to decompose the problem into several binary subproblems, reducing the complexity and, consequently, dividing the effects caused by noise into each of these subproblems. This paper analyzes the usage of decomposition strategies, and more specifically the One-vs-One scheme, to deal with noisy multi-class datasets. In order to investigate whether the decomposition is able to reduce the effect of noise or not, a large number of datasets are created introducing different levels and types of noise, as suggested in the literature. Several well-known classification algorithms, with or without decomposition, are trained on them in order to check when decomposition is advantageous. The results obtained show that methods using the One-vs-One strategy lead to better performances and more robust classifiers when dealing with noisy data, especially with the most disruptive noise schemes.

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Notes

  1. We refer to clean and noise-free datasets to the original datasets without additional induced noise, despite they might have noise, but it is not quantifiable, and hence it cannot be used to evaluate the robustness of the methods against noise.

  2. http://www.keel.es/datasets.php.

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Acknowledgments

Supported by the Spanish Ministry of Science and Technology under Project TIN2011-28488, and also by the Regional Projects P10-TIC-06858 and P11-TIC-9704. José A. Sáez holds an FPU scholarship from the Spanish Ministry of Education and Science.

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Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, CITIC-UGR, 18071 , Granada, Spain

    José A. Sáez & Francisco Herrera

  2. Department of Automática y Computación, Universidad Pública de Navarra, 31006, Pamplona, Spain

    Mikel Galar

  3. Department of Civil Engineering, LSI, University of Burgos, 09006, Burgos, Spain

    Julián Luengo

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  1. José A. Sáez
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Correspondence to José A. Sáez.

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Sáez, J.A., Galar, M., Luengo, J. et al. Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition. Knowl Inf Syst 38, 179–206 (2014). https://doi.org/10.1007/s10115-012-0570-1

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