Learning to Predict Rare Events in Event Sequences
Learning to Predict Rare Events in Event Sequences
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发表时间：
1998-08
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通讯作者：
Gary M. Weiss;H. Hirsh
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作者：
Gary M. Weiss;H. Hirsh
学习从具有分类特征的事件序列中预测罕见事件是一个重要的现实问题，现有的统计和机器学习方法并不适合解决这个问题。本文介绍了timeweaver，基于遗传算法的机器学习系统，通过识别预测的时间和顺序模式来预测罕见的事件。TimeWeaver被应用于从110，000个警报消息中预测电信设备故障的任务，并显示出优于现有的学习方法。
Learning to predict rare events from sequences of events with categorical features is an important, real-world, problem that existing statistical and machine learning methods are not well suited to solve. This paper describes timeweaver, a genetic algorithm based machine learning system that predicts rare events by identifying predictive temporal and sequential patterns. Timeweaver is applied to the task of predicting telecommunication equipment failures from 110,000 alarm messages and is shown to outperform existing learning methods.