ACE: Anomaly Clustering Ensemble for Multi-perspective Anomaly Detection in Robot Behaviors
ACE: Anomaly Clustering Ensemble for Multi-perspective Anomaly Detection in Robot Behaviors
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DOI：
10.1137/1.9781611972818.1
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
S. Ando;Einoshin Suzuki;Y. Seki;Theerasak Thanongphongphan;Daisuke Hoshino
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文献类型：
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作者：
S. Ando;Einoshin Suzuki;Y. Seki;Theerasak Thanongphongphan;Daisuke Hoshino
研究了时间序列序列异常检测在自主机器人行为中的应用。时序数据挖掘的一个重要方面是时序参数的选择，如子序列长度和平滑度。例如，在手头的任务中，机器人的速度模式，这是它的基本特征之一，根据测量位移的间隔而显著变化。选择时间尺度和分辨率在无监督环境中是困难的，并且通常比方法的选择更关键。在本文中，我们提出了一个集成框架聚合异常检测从不同的角度，即，用户定义的时间参数的设置。在所提出的框架中，每个行为都被标记为在多个设置中是否异常。标签的集合被用作相应行为的元特征。元特征空间中的聚类分析划分了与特定参数范围相关的异常行为。该框架还包括基于实例的异常检测的可扩展实现。我们评估所提出的框架ROC分析，在比较传统的集成异常检测方法。
This paper addresses an application of anomaly detection from subsequences of time series (STS) to autonomous robots’ behaviors. An important aspect of mining sequential data is selecting the temporal parameters, such as the subsequence length and the degree of smoothing. For example in the task at hand, the patterns of the robot’s velocity, which is one of its fundamental features, vary significantly subject to the interval for measuring the displacement. Selecting the time scale and resolution is difficult in unsupervised settings, and is often more critical than the choice of the method. In this paper, we propose an ensemble framework for aggregating anomaly detection from different perspectives, i.e., settings of user-defined, temporal parameters. In the proposed framework, each behavior is labeled whether it is an anomaly in multiple settings. The set of labels are used as meta-features of the respective behaviors. Cluster analysis in a meta-feature space partitions anomalous behaviors pertained to a specific range of parameters. The framework also includes a scalable implementation of the instance-based anomaly detection. We evaluate the proposed framework by ROC analysis, in comparison to conventional ensemble methods for anomaly detection.