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Rough Nearest Neighbour Classifier

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Rough Set–Based Classification Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 802))

Abstract

The nearest neighbour algorithm is an efficient but relatively time–consuming method of non-parametric regression and classification [9]. The method can be easily adapted to work with missing data using simple marginalisation or other preprocessing [1, 8, 10, 12]. Moreover, the efficiency of this solution is really high. In this chapter, a rough version of the algorithm will be presented. At the beginning, the basic version of the k-nearest neighbour classifier will be recalled, and then a rough version prepared for missing data will be proposed.

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Authors and Affiliations

  1. Institute of Computational Intelligence, Częstochowa University of Technology, Częstochowa, Poland

    Robert K. Nowicki

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  1. Robert K. Nowicki
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Correspondence to Robert K. Nowicki .

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Nowicki, R.K. (2019). Rough Nearest Neighbour Classifier. In: Rough Set–Based Classification Systems. Studies in Computational Intelligence, vol 802. Springer, Cham. https://doi.org/10.1007/978-3-030-03895-3_6

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