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• W2098493663 abstract "Nearest prototype methods are a successful trend of many pattern classification tasks. However, they present several shortcomings such as time response, noise sensitivity, and storage requirements. Data reduction techniques are suitable to alleviate these drawbacks. Prototype generation is an appropriate process for data reduction, which allows the fitting of a dataset for nearest neighbor (NN) classification. This brief presents a methodology to learn iteratively the positioning of prototypes using real parameter optimization procedures. Concretely, we propose an iterative prototype adjustment technique based on differential evolution. The results obtained are contrasted with nonparametric statistical tests and show that our proposal consistently outperforms previously proposed methods, thus becoming a suitable tool in the task of enhancing the performance of the NN classifier." @default.
• W2098493663 created "2016-06-24" @default.
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• W2098493663 date "2010-12-01" @default.
• W2098493663 modified "2023-10-18" @default.
• W2098493663 title "IPADE: Iterative Prototype Adjustment for Nearest Neighbor Classification" @default.
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• W2098493663 doi "https://doi.org/10.1109/tnn.2010.2087415" @default.
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