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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >A Semisupervised Approach to the Detection and Characterization of Outliers in Categorical Data
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A Semisupervised Approach to the Detection and Characterization of Outliers in Categorical Data

机译：分类数据中异常值的检测和表征的半监督方法

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摘要

In this paper, we introduce a new approach of semisupervised anomaly detection that deals with categorical data. Given a training set of instances (all belonging to the normal class), we analyze the relationship among features for the extraction of a discriminative characterization of the anomalous instances. Our key idea is to build a model that characterizes the features of the normal instances and then use a set of distance-based techniques for the discrimination between the normal and the anomalous instances. We compare our approach with the state-of-the-art methods for semisupervised anomaly detection. We empirically show that a specifically designed technique for the management of the categorical data outperforms the general-purpose approaches. We also show that, in contrast with other approaches that are opaque because their decision cannot be easily understood, our proposed approach produces a discriminative model that can be easily interpreted and used for the exploration of the data.
机译：在本文中，我们介绍了一种处理分类数据的半监督异常检测新方法。给定一组训练好的实例（全部属于正常类），我们分析特征之间的关系，以提取异常实例的判别特征。我们的关键思想是建立一个描述正常实例特征的模型，然后使用一套基于距离的技术来区分正常实例和异常实例。我们将我们的方法与用于半监督异常检测的最新方法进行了比较。我们凭经验表明，专门设计的用于管理分类数据的技术要优于通用方法。我们还表明，与其他不透明的方法（由于其决策不易理解）相比，我们提出的方法可生成可轻松解释并用于数据探索的判别模型。

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