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  1. Valero-Mas, J.J.; Calvo-Zaragoza, J.; Rico-Juan, J.R.; Iñesta, J.M.
    "An Experimental Study on Rank Methods for Prototype Selection"
    Soft Computing, vol. 21, pp. 5703-–5715 (2017)
    : bibtex : pdf : DOI

    Prototype selection is one of the most popular approaches for addressing the low efficiency issue typically found in the well-known k-Nearest Neighbour classification rule. These techniques select a representative subset from an original collection of prototypes with the premise of main- taining the same classification accuracy. Most recently, rank methods have been proposed as an alternative to develop new selection strategies. Following a certain heuristic, these methods sort the elements of the initial collection accord- ing to their relevance and then select the best possible subset by means of a parameter representing the amount of data to maintain. Due to the relative novelty of these methods, their performance and competitiveness against other strategies is still unclear. This work performs an exhaustive experimental study of such methods for prototype selection. A represen- tative collection of both classic and sophisticated algorithms are compared to the aforementioned techniques in a num- ber of datasets, including different levels of induced noise. Results report the remarkable competitiveness of these rank methods as well as their excellent trade-off between proto- type reduction and achieved accuracy.

@article {
 author = "Valero-Mas, J.J.; Calvo-Zaragoza, J.; Rico-Juan, J.R.; Iñesta, J.M.",
 title  = "An Experimental Study on Rank Methods for Prototype Selection",
 issn = "1432-7643",
 journal = "Soft Computing",
 number = "19",
 pages = "5703-–5715",
 volume = "21",
 year = "2017"
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