Active Diagnosis via AUC Maximization: An Efficient Approach for Multiple Fault Identification in Large Scale, Noisy Networks

Details

• Personal Author:
  Bellala G ; Bhavnani SK ; Scott C ; Stanley J
• Description:
  The problem of active diagnosis arises in several applications such as disease diagnosis, and fault diagnosis in computer networks, where the goal is to rapidly identify the binary states of a set of objects (e.g., faulty or working) by sequentially selecting, and observing, (noisy) responses to binary valued queries. Current algorithms in this area rely on loopy belief propagation for active query selection. These algorithms have an exponential time complexity, making them slow and even intractable in large networks. We propose a rank-based greedy algorithm that sequentially chooses queries such that the area under the ROC curve of the rank-based output is maximized. The AUC criterion allows us to make a simplifying assumption that significantly reduces the complexity of active query selection (from exponential to near quadratic), with little or no compromise on the performance quality. [Description provided by NIOSH]
• Subjects:
  Decision Making Diagnosis Occupational Health Safety Software
• Keywords:
  Algorithms Computerized Simulation Computer Software Decision Making Diagnostic Techniques
• ISBN:
  9780974903972
• Publisher:
  Association for Uncertainty in Artificial Intelligence
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Proceedings
• Place as Subject:
  Michigan ; OSHA Region 5 ; OSHA Region 6 ; Texas
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  1-8
• NIOSHTIC Number:
  nn:20055939
• Citation:
  Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), July 14-17, 2011, Barcelona. Corvallis, OR: Association for Uncertainty in Artificial Intelligence 2011 Jul; :1-8
• Federal Fiscal Year:
  2011
• NORA Priority Area:
  Services
• Performing Organization:
  University of Texas Medical Branch, Galveston
• Peer Reviewed:
  False
• Start Date:
  20100901
• Source Full Name:
  Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), July 14-17, 2011, Barcelona
• End Date:
  20130131
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:300031cc6d80b9425bae2950adcfa98ed6178eeba9ec2d7eae4483623534dda8f327768279bc4bff1e62cd813c418796de4385e44fdd97969fdf84a354429941
• Download URL:
  https://stacks.cdc.gov/view/cdc/213687/cdc_213687_DS1.pdf
• File Type:
  [PDF - 167.44 KB ]