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Combining adaptive vector quantization and prototype selection techniques to improve nearest neighbour classifiers
Kybernetika, Tome 34 (1998) no. 4, pp. 405-410
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Prototype Selection (PS) techniques have traditionally been applied prior to Nearest Neighbour (NN) classification rules both to improve its accuracy (editing) and to alleviate its computational burden (condensing). Methods based on selecting/discarding prototypes and methods based on adapting prototypes have been separately introduced to deal with this problem. Different approaches to this problem are considered in this paper and their main advantages and drawbacks are pointed out along with some suggestions for their joint application in some cases.
Prototype Selection (PS) techniques have traditionally been applied prior to Nearest Neighbour (NN) classification rules both to improve its accuracy (editing) and to alleviate its computational burden (condensing). Methods based on selecting/discarding prototypes and methods based on adapting prototypes have been separately introduced to deal with this problem. Different approaches to this problem are considered in this paper and their main advantages and drawbacks are pointed out along with some suggestions for their joint application in some cases.
Classification : 62H30, 68P30, 68T10, 68U10
Keywords: nearest neighbour classification; prototype selection 
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     author = {Ferri, Francesc J.},
     title = {Combining adaptive vector quantization and prototype selection techniques to improve nearest neighbour classifiers},
     journal = {Kybernetika},
     pages = {405--410},
     year = {1998},
     volume = {34},
     number = {4},
     zbl = {1274.68382},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/KYB_1998_34_4_a8/}
}
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Ferri, Francesc J. Combining adaptive vector quantization and prototype selection techniques to improve nearest neighbour classifiers. Kybernetika, Tome 34 (1998) no. 4, pp. 405-410. http://geodesic.mathdoc.fr/item/KYB_1998_34_4_a8/

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