Active diagnosis via AUC maximization: An efficient approach for multiple fault identification in large scale, noisy networks

Gowtham Bellala, Jason Stanley, Clayton Scott, Suresh K. Bhavnani

Research output: Contribution to conferencePaper

2 Scopus citations

Abstract

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 ob-serving, (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 se-quentially 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.

Original languageEnglish (US)
Pages35-42
Number of pages8
StatePublished - Sep 29 2011
Event27th Conference on Uncertainty in Artificial Intelligence, UAI 2011 - Barcelona, Spain
Duration: Jul 14 2011Jul 17 2011

Other

Other27th Conference on Uncertainty in Artificial Intelligence, UAI 2011
CountrySpain
CityBarcelona
Period7/14/117/17/11

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics

Cite this

Bellala, G., Stanley, J., Scott, C., & Bhavnani, S. K. (2011). Active diagnosis via AUC maximization: An efficient approach for multiple fault identification in large scale, noisy networks. 35-42. Paper presented at 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011, Barcelona, Spain.