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    Statistical-mean double-quantitative K-nearest neighbor …

    • Neighborhood granulation underlies neighborhood rough sets, and it also induces the basic classifier of K-nearest neighbor (KNN). Based on neighborhood granulation and its distance measureme… 展开

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    Construct arithmetic-mean, geometric-mean double-quantitative distances, classifiers.
    ••… 展开

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    Introduction

    The methodology of rough sets facilitates uncertainty measurement and knowledge reasoning [… 展开

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    Three-level structuring of neighborhood system

    Neighborhood systems and their granularity structuring underlie neighborhood rough sets and relevant applications, and thus they are reviewed [26], [47].
    Neighborhood sy… 展开

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    Double-quantitative distances based on neighborhood granulation

    Distance constructions play an important role in uncertainty measurement, and they underlie the information extraction and intelligence application. In this section, Db-Quant distances … 展开

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    Classifier designs and learning algorithms based on statistical-mean double-quantitative distances

    Neighborhood granulation and measurement naturally underlie classifier designs for classification learning. Thus, multiple classifiers already exist and can be further develope… 展开

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