Active Diagnosis Under Persistent Noise with Unknown Noise Distribution: A Rank-Based Approach

Details

• Personal Author:
  Bellala G ; Bhavnani SK ; Scott C
• Description:
  We consider a problem of active diagnosis, where the goal is to efficiently identify an unknown object by sequentially selecting, and observing, the responses to binary valued queries. We assume that query observations are noisy, and further that the noise is persistent, meaning that repeating a query does not change the response. Previous work in this area either assumed the knowledge of the query noise distribution, or that the noise level is sufficiently low so that the unknown object can be identified with high accuracy. We make no such assumptions, and introduce an algorithm that returns a ranked list of objects, such that the expected rank of the true object is optimized. Furthermore, our algorithm does not require knowledge of the query noise distribution. [Description provided by NIOSH]
• Subjects:
  Decision Making Diagnosis Occupational Health Safety Software
• Keywords:
  Algorithms Computerized Simulation Computer Software Decision Making Diagnostic Techniques
• ISSN:
  1533-7928
• 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:
  155-163
• Volume:
  15
• NIOSHTIC Number:
  nn:20055938
• Citation:
  J Mach Learn Res 2011 Aug; 15:155-163
• Federal Fiscal Year:
  2011
• Performing Organization:
  University of Texas Medical Branch, Galveston
• Peer Reviewed:
  False
• Start Date:
  20100901
• Source Full Name:
  Journal of Machine Learning Research. Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS), April 11-13, 2011, Fort Lauderdale, Florida
• End Date:
  20130131
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:3fdce601b108eebcdd3be4ccc4da0fa5c1f587f3497dc8ba7624cc2ac31ceaeaca7813fdfeac595ad3cf419cdc3a49ca877424d3b407b2f1702fd39892a2db5e
• Download URL:
  https://stacks.cdc.gov/view/cdc/213686/cdc_213686_DS1.pdf
• File Type:
  [PDF - 1.34 MB ]