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    • 92. 发明授权
    • Method for visualizing feature ranking of a subset of features for classifying data using a learning machine
    • 用于使用学习机分类数据的特征子集的特征排名可视化的方法
    • US08126825B2
    • 2012-02-28
    • US13079198
    • 2011-04-04
    • Isabelle Guyon
    • Isabelle Guyon
    • G06F15/18
    • G06F19/24G06F19/20G06F19/28G06K9/6231G06K9/6269G06N99/005
    • A method for enhancing knowledge discovery from a dataset uses visualization of a subset features within a dataset that provide the best separation of the dataset into classes. One or more classifiers are trained using each subset of features and the success rate of the classifiers in accurately classifying the dataset is calculated. The success rate is converted into a ranking that is represented as a visually distinguishable characteristic. One or more tree structures may be displayed with a node representing each feature, and the visually distinguishable characteristic is used to indicate the scores for each feature subset. Connectors between the nodes may be used to indicate unconstrained and constrained feature sets. Nodes within a constrained path may be substituted for a feature within the preferred, unconstrained path if that feature is impractical to measure.
    • 从数据集中增强知识发现的方法使用数据集中的子集特征的可视化来提供数据集到类中的最佳分离。 使用每个特征子集训练一个或多个分类器,并计算分类器在准确分类数据集中的成功率。 成功率被转换为视觉上可区分的特征的排名。 一个或多个树结构可以与表示每个特征的节点一起显示,并且可视区分特征用于指示每个特征子集的分数。 节点之间的连接器可用于指示无约束和约束特征集。 受限路径内的节点可以替代优选的无约束路径中的特征,如果该特征不可测量。