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Automated Data Cleaning via Multi-View Anomaly Detection
Thomas Dietterich

Sat Dec 10 12:10 AM -- 12:50 AM (PST) @

One of the first steps in the data analysis pipeline is data cleaning: detecting data from failed sensors. This talk will discuss the application of anomaly detection algorithms to find and remove bad readings from weather station data. We will review our previous work on DBN time series models and our current work on applying non-parametric anomaly detection algorithms as part of our SENSOR-DX multi-view anomaly detection architecture. A major challenge in evaluating these algorithms is to obtain ground truth, because real sensor data tends to be labeled conservatively by domain experts.

Author Information

Thomas Dietterich (Oregon State University)

Tom Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stanford University 1984) is Professor and Director of Intelligent Systems Research at Oregon State University. Among his contributions to machine learning research are (a) the formalization of the multiple-instance problem, (b) the development of the error-correcting output coding method for multi-class prediction, (c) methods for ensemble learning, (d) the development of the MAXQ framework for hierarchical reinforcement learning, and (e) the application of gradient tree boosting to problems of structured prediction and latent variable models. Dietterich has pursued application-driven fundamental research in many areas including drug discovery, computer vision, computational sustainability, and intelligent user interfaces. Dietterich has served the machine learning community in a variety of roles including Executive Editor of the Machine Learning journal, co-founder of the Journal of Machine Learning Research, editor of the MIT Press Book Series on Adaptive Computation and Machine Learning, and editor of the Morgan-Claypool Synthesis series on Artificial Intelligence and Machine Learning. He was Program Co-Chair of AAAI-1990, Program Chair of NIPS-2000, and General Chair of NIPS-2001. He was first President of the International Machine Learning Society (the parent organization of ICML) and served a term on the NIPS Board of Trustees and the Council of AAAI.

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