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    • 14. 发明申请
    • DATA CLASSIFICATION METHOD FOR UNKNOWN CLASSES
    • 未知类别的数据分类方法
    • US20100198758A1
    • 2010-08-05
    • US12364442
    • 2009-02-02
    • Chetan Kumar GuptaAbhay MehtaSong Wang
    • Chetan Kumar GuptaAbhay MehtaSong Wang
    • G06F15/18
    • G06N20/00
    • A system and method for creating a CD Tree for data having unknown classes are provided. Such a method can include dividing training data into a plurality of subsets of node training data at a plurality of nodes arranged in a hierarchical arrangement, wherein the node training data has a range. Furthermore, dividing node training data at each node can include, ordering the node training data, generating a plurality of separation points and a plurality of pairs of bins from the node training data, wherein each pair of bins includes a first bin and a second bin with a separation point being located between the first bin and the second bin, and classifying the node training data into either the first bin or the second bin for each of the separation points, wherein the classifying is based on a data classifier. Validation data can be utilized to calculate the bin accuracy between the node training data bin pairs and the validation data bin pairs for each separation point, and the separation point having a high bin accuracy can be selected as the node separation point.
    • 提供了一种用于为具有未知类的数据创建CD树的系统和方法。 这种方法可以包括将训练数据划分为以分层布置排列的多个节点的节点训练数据的多个子集,其中节点训练数据具有范围。 此外,在每个节点处划分节点训练数据可以包括:从节点训练数据生成节点训练数据,生成多个分离点和多对分组,其中每对分组包括第一分组和第二分组 其中分离点位于第一仓和第二仓之间,并且将节点训练数据分类为用于每个分离点的第一仓或第二仓,其中分类基于数据分类器。 可以使用验证数据来计算节点训练数据箱对与每个分离点的验证数据箱对之间的仓精度,并且可以选择具有高仓精度的分离点作为节点分离点。