TIME-BASED ENSEMBLE MACHINE LEARNING MODEL

• Main Authors: AMBATI SRISATISH, BARTHUR ASHRITH
• Format: Patent
• Language: Chinese; English
• Published: 29.11.2019
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; PHYSICS

An input dataset is sorted into a first version of data and a second version of data. The first version of data is associated with a first period of time and the second version of data is associated with a second period of time. The second period of time is a shorter period of time than the first period of time. A first set of one or more machine learning models is generated based on the first version of data. A second set of one or more machine learning models is generated based on the second version of data. The first set of one or more machine learning models and the second set of one or more machine learning models are combined to generate an ensemble model. A prediction based on the ensemble model is outputted. The prediction indicates abnormal behavior associated with the input dataset. 将输入数据集分类成数据的第一版本和数据的第二版本。数据的第一版本与第一时间段相关联，并且数据的第二版本与第二时间段相关联。所述第二时间段是比第一时间段短的时间段。基于所述数据的第一版本来生成一个或多个机器学习模型的第一集合。基于所述数据的第二版本来生成一个或多个机器学习模型的第二集合。组合一个或多个机器学习模型的第一集合与一个或多个机器学习模型的第二集合来生成全体模型。输出基于全体模型的预测。预测指示与输入数据集相关联的异常行为。