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• W4309747605 abstract "The unceasing increase of data quantity severely limits the wide application of mature classification algorithms due to the unacceptable execution time and the insufficient memory. How to fast incrementally obtain high decision reference set and adapt to incremental data environment is urgently needed in incremental environments, large dataset, etc. This paper proposes a novel prototype selection algorithm by integrating the strategies between condensing method and editing method. To an unlearned pattern, this algorithm extends the references scope from its single nearest neighbour to its k nearest neighbourhood that can expand the judgment information to obtain its detailed neighbour relationship. Then a pattern was determined whether it is a prototype using its neighbour relationship and classification boundary asymptotically strategy. To maintain the higher reference set, this algorithm periodically updates those prototypes that locates in the non-boundary zone or is long-time unlearned. The empirical study shows that this algorithm obtains the smaller and higher boundary prototypes without decreasing classification accuracy and reduction rate than the compared algorithms." @default.
• W4309747605 created "2022-11-29" @default.
• W4309747605 creator A5004169135 @default.
• W4309747605 creator A5091818964 @default.
• W4309747605 date "2022-11-22" @default.
• W4309747605 modified "2023-09-26" @default.
• W4309747605 title "Fast prototype selection algorithm based on adjacent neighbourhood and boundary approximation" @default.
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• W4309747605 doi "https://doi.org/10.1038/s41598-022-23036-9" @default.
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