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lstm自动编码器以进行异常检测:使用LSTM自动编码器检测异常并对稀有事件进行分类-源码

于 2021-11-07 发布
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LSTM异常检测自动编码器 使用LSTM自动编码器检测异常并对稀有事件进行分类。 如此多次,实际上是现实生活中的大多数数据,我们都有不平衡的数据。 数据是我们最感兴趣的事件,很少见,不像正常情况下那么频繁。 例如,在欺诈检测中。 大多数数据是正常情况,无论数据是否已标记,我们都希望检测异常或何时发生欺诈。 处理未标记的数据时,我们通常会使用“异常值检测”方法,例如,基于聚类的局部离群值因子(CBLOF) ,基于直方图的离群值检测(HBOS)等。而标记的数据被视为“分类”问题和分类器,如和。 但是,由于正数据点在数据中很少见,因此该算法发现很难从数据中学到很多东西。 例如,分类器通常最终会在所有情况下都预测为“负”,以实现最佳准确性。 在这里,我们将研究可用于有监督和无监督以及罕见事件分类问题的另一种方法。 长短期记忆自动编码器。

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