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- W2004656598 abstract "Some of the most commonly used classifiers are based on the retrieval and examination of the k Nearest Neighbors of unclassified instances. However, since the size of datasets can be large, these classifiers are inapplicable when the time-costly sequential search over all instances is used to find the neighbors. The Minimum Distance Classifier is a very fast classification approach but it usually achieves much lower classification accuracy than the k-NN classifier. In this paper, a fast, hybrid and model-free classification algorithm is introduced that combines the Minimum Distance and the k-NN classifiers. The proposed algorithm aims at maximizing the reduction of computational cost, by keeping classification accuracy at a high level. The experimental results illustrate that the proposed approach can be applicable in dynamic, time-constrained environments." @default.
- W2004656598 created "2016-06-24" @default.
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- W2004656598 date "2011-06-30" @default.
- W2004656598 modified "2023-09-28" @default.
- W2004656598 title "A fast hybrid classification algorithm based on the minimum distance and the k-NN classifiers" @default.
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- W2004656598 doi "https://doi.org/10.1145/1995412.1995430" @default.
- W2004656598 hasPublicationYear "2011" @default.
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