Abstract
In this paper, we present an extension of k nearest neighbors method so it can perform imputation/classification from datasets with low quality data. The method performs a weighting of neighbors based on their imperfection and distance of classes. Thus the method allows us explicitly to indicate the average degree of imperfection of the neighbors that it is accepted to carry out the imputation/classification and the average distance of classes to the class of example to impute/classify that it is allowed. We carry out several experiments with both real-world and synthetic datasets with low quality data to test the proposed method.
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Acknowledgements
Supported by the projects TIN2011-27696-C02-02, TIN2014-52099-R and TIN2014-56381-REDT (“Red de Lógica Difusa y Soft Computing (LODISCO)”) of the Ministry of Economy and Competitiveness of Spain.
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Cadenas, J.M., Garrido, M.C., Martínez, R. (2015). Measuring Data Imperfection in a Neighborhood Based Method. In: Puerta, J., et al. Advances in Artificial Intelligence. CAEPIA 2015. Lecture Notes in Computer Science(), vol 9422. Springer, Cham. https://doi.org/10.1007/978-3-319-24598-0_19
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DOI: https://doi.org/10.1007/978-3-319-24598-0_19
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