Artikel in Tagungsbänden
Towards Few-Shot Time Series Anomaly Detection with Temporal Attention and Dynamic Thresholding.
In:
International Conference on Machine Learning and Applications (ICMLA), Seiten 1444-1450.
IEEE, 2023.
Chandana Priya Nivarthi und Bernhard Sick.
[Kurzfassung]
[BibTeX]
Anomaly detection plays a pivotal role in diverse realworld applications such as cybersecurity, fault detection, network monitoring, predictive maintenance, and highly automated driving. However, obtaining labeled anomalous data can be a formidable challenge, especially when anomalies exhibit temporal evolution. This paper introduces LATAM (Long short-term memory Autoencoder with Temporal Attention Mechanism) for few-shot anomaly detection, with the aim of enhancing detection performance in scenarios with limited labeled anomaly data. LATAM effectively captures temporal dependencies and emphasizes significant patterns in multivariate time series data. In our investigation, we comprehensively evaluate LATAM against other anomaly detection models, particularly assessing its capability in few-shot learning scenarios where we have minimal examples from the normal class and none from the anomalous class in the training data. Our experimental results, derived from real-world photovoltaic inverter data, highlight LATAM’s superiority, showcasing a substantial 27% mean F1 score improvement, even when trained on a mere two-week dataset. Furthermore, LATAM demonstrates remarkable results on the open-source SWaT dataset, achieving a 12% boost in accuracy with only two days of training data. Moreover, we introduce a simple yet effective dynamic thresholding mechanism, further enhancing the anomaly detection capabilities of LATAM. This underscores LATAM’s efficacy in addressing the challenges posed by limited labeled anomalies in practical scenarios and it proves valuable for downstream tasks involving temporal representation and time series prediction, extending its utility beyond anomaly detection applications.
Never-Ending Learning.
In:
AAAI.
2015.
: Never-Ending Learning in AAAI-2015
T. Mitchell, W. Cohen, E. Hruscha, P. Talukdar, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohammad, N. Nakashole, E. Platanios, A. Ritter, M. Samadi, B. Settles, R. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves und J. Welling.
[doi]
[BibTeX]
Artikel in Zeitschriften
Human-level control through deep reinforcement learning.
Nature, 518(7540):529-533, 2015.
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg und Demis Hassabis.
[doi]
[BibTeX]
Artikel in Tagungsbänden
ConDist: A Context-Driven Categorical Distance Measure.
In: ECMLPKDD2015
(Herausgeber): .
2015.
Markus Ring, Florian Otto, Martin Becker, Thomas Niebler, Dieter Landes und Andreas Hotho.
[BibTeX]
Large-scale factorization of type-constrained multi-relational data.
In:
International Conference on Data Science and Advanced Analytics, DSAA 2014, Shanghai, China, October 30 - November 1, 2014, Seiten 18-24.
IEEE, 2014.
Denis Krompass, Maximilian Nickel und Volker Tresp.
[doi]
[BibTeX]
Buchbeiträge
An Introduction to Ontology Learning.
In:
J. Lehmann und J. Voelker (Herausgeber):
Perspectives on Ontology Learning, Seiten ix-xvi.
AKA / IOS Press, 2014.
Jens Lehmann und Johanna Voelker.
[doi]
[BibTeX]
Artikel in Zeitschriften
Exploiting Structural Consistencies with Stacked Conditional Random Fields.
Mathematical Methodologies in Pattern Recognition and Machine Learning Springer Proceedings in Mathematics & Statistics, 30:111-125, 2013.
Peter Kluegl, Martin Toepfer, Florian Lemmerich, Andreas Hotho und Frank Puppe.
[Kurzfassung]
[BibTeX]
Conditional Random Fields (CRF) are popular methods for labeling unstructured or textual data. Like many machine learning approaches, these undirected graphical models assume the instances to be independently distributed. However, in real-world applications data is grouped in a natural way, e.g., by its creation context. The instances in each group often share additional structural consistencies. This paper proposes a domain-independent method for exploiting these consistencies by combining two CRFs in a stacked learning framework. We apply rule learning collectively on the predictions of an initial CRF for one context to acquire descriptions of its specific properties. Then, we utilize these descriptions as dynamic and high quality features in an additional (stacked) CRF. The presented approach is evaluated with a real-world dataset for the segmentation of references and achieves a significant reduction of the labeling error.
Sonstiges
Large-scale Multi-label Learning with Missing Labels.
2013. cite arxiv:1307.5101.
Hsiang-Fu Yu, Prateek Jain, Purushottam Kar und Inderjit S. Dhillon.
[doi]
[Kurzfassung]
[BibTeX]
The multi-label classification problem has generated significant interest in recent years. However, existing approaches do not adequately address two key challenges: (a) the ability to tackle problems with a large number (say millions) of labels, and (b) the ability to handle data with missing labels. In this paper, we directly address both these problems by studying the multi-label problem in a generic empirical risk minimization (ERM) framework. Our framework, despite being simple, is surprisingly able to encompass several recent label-compression based methods which can be derived as special cases of our method. To optimize the ERM problem, we develop techniques that exploit the structure of specific loss functions - such as the squared loss function - to offer efficient algorithms. We further show that our learning framework admits formal excess risk bounds even in the presence of missing labels. Our risk bounds are tight and demonstrate better generalization performance for low-rank promoting trace-norm regularization when compared to (rank insensitive) Frobenius norm regularization. Finally, we present extensive empirical results on a variety of benchmark datasets and show that our methods perform significantly better than existing label compression based methods and can scale up to very large datasets such as the Wikipedia dataset.
Artikel in Tagungsbänden
Dynamic Auto-Encoders for Semantic Indexing.
In: P. of the NIPS 2010 Workshop on Deep Learning
(Herausgeber): .
2010.
Piotr Mirowski, Marc'Aurelio Ranzato und Yann LeCun.
[doi]
[BibTeX]
Artikel in Zeitschriften
Random Forests.
Machine Learning, 45(1):5-32, 2001.
Leo Breiman.
[doi]
[Kurzfassung]
[BibTeX]
Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to