Enhancing IPADE algorithm with a different individual codification
作者： Isaac Triguero , Salvador García , Francisco Herrera
DOI: 10.1007/978-3-642-21222-2_32
关键词:
摘要: Nearest neighbor is one of the most used techniques for performing classification tasks. However, its simplest version has several drawbacks, such as low efficiency, storage requirements and sensitivity to noise. Prototype generation an appropriate process alleviate these drawbacks that allows fitting a data set nearest classification. In this work, we present extension our previous proposal called IPADE, methodology learn iteratively positioning prototypes using differential evolution algorithm. extension, which have IPADECS, complete solution codified in each individual. The results are contrasted with non-parametrical statistical tests show outperforms previously proposed methods.
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