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SYSTEM AND METHOD FOR HIERARCHICALLY BUILDING PREDICTIVE ANALYTIC MODELS ON A DATASET

机译:在数据集上分层构建预测分析模型的系统和方法

摘要

Predictive analytic models are hierarchically built based on a training dataset, which includes pairs of input data and output data. First, the input data and the output data are preprocessed. A hierarchical clustering process is performed on the dataset. The hierarchical clustering process comprises level-1 input and output data clustering, level-2 input and output data clustering, and so on, up to level-K input and output data clustering, where K is an integer greater than one. A hierarchical model building process is performed. The hierarchical model building process comprises level-1 model building over level-1 clustered input and output data, level-2 model building over level-2 clustered input and output data, and so on, up to level-K model building over level-K clustered input and output data. At least one level-K predictive model is generated as the resulting built model.
机译:预测分析模型是基于训练数据集分层构建的,该训练数据集包括输入数据和输出数据对。首先,对输入数据和输出数据进行预处理。对数据集执行分层聚类过程。分层聚类过程包括1级输入和输出数据聚类,2级输入和输出数据聚类,等等,直至K级输入和输出数据聚类,其中K是大于1的整数。执行分层模型构建过程。分层模型的构建过程包括:基于1级群集输入和输出数据的1级模型构建,基于2级群集输入和输出数据的2级模型构建,依此类推,直到在-2级群集中的K级模型构建。 K个聚集的输入和输出数据。生成至少一个K级预测模型作为生成的模型。

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