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An incremental prototype set building technique
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DOI：10.1016/S0031-3203(00)00184-9.png)
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摘要
En 中文
This paper deals with the task of finding a set of prototypes from the training set. A reduced set is obtained which is used instead of the training set when nearest neighbour classification is used. Prototypes are added in an incremental fashion, where at each step of the algorithm, the number of prototypes selected keeps on increasing. The number of patterns in the training data classified correctly also keeps on increasing till all patterns are classified properly. After this, a deletion operator is used where some prototypes which are not so useful are removed. This method has been used to obtain the prototypes for a variety of benchmark data sets and results have been presented. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keyword:
pattern classification
prototype selection
supervised learning
k-nearest neighbour
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期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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