ACE: Anomaly clustering ensemble for multi-perspective anomaly detection in robot behaviors

Shin Ando, Theerasak Thanomphongphan, Daisuke Hoshino, Yoichi Seki, Einoshin Suzuki

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

8 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルProceedings of the 11th SIAM International Conference on Data Mining, SDM 2011
出版社Society for Industrial and Applied Mathematics Publications
ページ1-12
ページ数12
ISBN(印刷版)9780898719925
DOI
出版ステータス出版済み - 2011
イベント11th SIAM International Conference on Data Mining, SDM 2011 - Mesa, AZ, 米国
継続期間: 4 28 20114 30 2011

出版物シリーズ

名前Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011

その他

その他11th SIAM International Conference on Data Mining, SDM 2011
Country米国
CityMesa, AZ
Period4/28/114/30/11

All Science Journal Classification (ASJC) codes

  • Software

フィンガープリント 「ACE: Anomaly clustering ensemble for multi-perspective anomaly detection in robot behaviors」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル