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    Methods for feature selection using classifier ensemble based genetic algorithms
    1.
    发明授权
    Methods for feature selection using classifier ensemble based genetic algorithms 有权
    使用基于分类器集合的遗传算法进行特征选择的方法

    公开(公告)号：US08762303B2

    公开(公告)日：2014-06-24

    申请号：US12441956

    申请日：2007-09-17

    申请人： Luyin Zhao ,  Lilla Boroczky ,  Lalitha A. Agnihotri ,  Michael C. Lee

    发明人： Luyin Zhao ,  Lilla Boroczky ,  Lalitha A. Agnihotri ,  Michael C. Lee

    IPC分类号： G06N3/12

    CPC分类号： G06N3/126 ,  G06F19/00 ,  G06K9/6217 ,  G06K2209/053 ,  G16H50/20 ,  G16H50/30

    摘要： Methods for performing genetic algorithm-based feature selection are provided herein. In certain embodiments, the methods include steps of applying multiple data splitting patterns to a learning data set to build multiple classifiers to obtain at least one classification result; integrating the at least one classification result from the multiple classifiers to obtain an integrated accuracy result; and outputting the integrated accuracy result to a genetic algorithm as a fitness value for a candidate feature subset, in which genetic algorithm-based feature selection is performed.

    摘要翻译： 本文提供了基于遗传算法的特征选择的方法。 在某些实施例中，所述方法包括将多个数据分割模式应用于学习数据集以构建多个分类器以获得至少一个分类结果的步骤; 整合来自多个分类器的至少一个分类结果以获得综合精度结果; 并将所述综合精度结果输出到遗传算法作为候选特征子集的适应度值，其中执行基于遗传算法的特征选择。

    METHODS FOR FEATURE SELECTION USING CLASSIFIER ENSEMBLE BASED GENETIC ALGORITHMS
    2.
    发明申请
    METHODS FOR FEATURE SELECTION USING CLASSIFIER ENSEMBLE BASED GENETIC ALGORITHMS 有权
    使用基于分类器ENSEMBLE的遗传算法进行特征选择的方法

    公开(公告)号：US20100036782A1

    公开(公告)日：2010-02-11

    申请号：US12441956

    申请日：2007-09-17

    申请人： Luyin Zhao ,  Lilla Boroczky ,  Lalitha A. Agnihotri ,  Michael C.C. Lee

    发明人： Luyin Zhao ,  Lilla Boroczky ,  Lalitha A. Agnihotri ,  Michael C.C. Lee

    IPC分类号： G06N3/12 ,  G06F19/00 ,  G06K9/00

    CPC分类号： G06N3/126 ,  G06F19/00 ,  G06K9/6217 ,  G06K2209/053 ,  G16H50/20 ,  G16H50/30

    摘要： Methods for performing genetic algorithm-based feature selection are provided herein. In certain embodiments, the methods include steps of applying multiple data splitting patterns to a learning data set to build multiple classifiers to obtain at least one classification result; integrating the at least one classification result from the multiple classifiers to obtain an integrated accuracy result; and outputting the integrated accuracy result to a genetic algorithm as a fitness value for a candidate feature subset, in which genetic algorithm-based feature selection is performed.

    摘要翻译： 本文提供了基于遗传算法的特征选择的方法。 在某些实施例中，所述方法包括将多个数据分割模式应用于学习数据集以构建多个分类器以获得至少一个分类结果的步骤; 整合来自多个分类器的至少一个分类结果以获得综合精度结果; 并将所述综合精度结果输出到遗传算法作为候选特征子集的适应度值，其中执行基于遗传算法的特征选择。