Ranking under uncertainty

Or Zuk*, Liat Ein-Dor, Eytan Domany

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Ranking objects is a simple and natural procedure for organizing data. It is often performed by assigning a quality score to each object according to its relevance to the problem at hand. Ranking is widely used for object selection, when resources are limited and it is necessary to select a subset of most relevant objects for further processing. In real world situations, the object's scores are often calculated from noisy measurements, casting doubt on the ranking reliability. We introduce an analytical method for assessing the influence of noise levels on the ranking reliability. We use two similarity measures for reliability evaluation, Top-K-List overlap and Kendall's τ measure, and show that the former is much more sensitive to noise than the latter. We apply our method to gene selection in a series of microarray experiments of several cancer types. The results indicate that the reliability of the lists obtained from these experiments is very poor, and that experiment sizes which are necessary for attaining reasonably stable Top-K-Lists are much larger than those currently available. Simulations support our analytical results.

Original languageAmerican English
Title of host publicationProceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007
Pages466-473
Number of pages8
StatePublished - 2007
Externally publishedYes
Event23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007 - Vancouver, BC, Canada
Duration: 19 Jul 200722 Jul 2007

Publication series

NameProceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007

Conference

Conference23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007
Country/TerritoryCanada
CityVancouver, BC
Period19/07/0722/07/07

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