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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Interactive Anomaly Detection With Reduced Expert Effort

Cheng, Lingyun, Sundaresh, Sadhana January 2020 (has links)
In several applications, when anomalies are detected, human experts have to investigate or verify them one by one. As they investigate, they unwittingly produce a label - true positive (TP) or false positive (FP). In this thesis, we propose two methods (PAD and Clustering-based OMD/OJRank) that exploit this label feedback to minimize the FP rate and detect more relevant anomalies, while minimizing the expert effort required to investigate them. These two methods iteratively suggest the top-1 anomalous instance to a human expert and receive feedback. Before suggesting the next anomaly, the methods re-ranks instances so that the top anomalous instances are similar to the TP instances and dissimilar to the FP instances. This is achieved by learning to score anomalies differently in various regions of the feature space (OMD-Clustering) and by learning to score anomalies based on the distance to the real anomalies (PAD). An experimental evaluation on several real-world datasets is conducted. The results show that OMD-Clustering achieves statistically significant improvement in both detection precision and expert effort compared to state-of-the-art interactive anomaly detection methods. PAD reduces expert effort but there was no improvement in detection precision compared to state-of-the-art methods. We submitted a paper based on the work presented in this thesis, to the ECML/PKDD Workshop on "IoT Stream for Data Driven Predictive Maintenance".

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