Efficiently learning the accuracy of labeling sources for selective sampling

Many scalable data mining tasks rely on active learning to provide the most useful accurately labeled instances. However, what if there are multiple labeling sources (`oracles' or `experts') with different but unknown reliabilities? With the recent advent of inexpensive and scalable online annotation tools, such as Amazon's Mechanical Turk, the labeling process has become more vulnerable to noise - and without prior knowledge of the accuracy of each individual labeler. This paper addresses exactly such a challenge: how to jointly learn the accuracy of labeling sources and obtain the most informative labels for the active learning task at hand minimizing total labeling effort. More specifically, we present IEThresh (Interval Estimate Threshold) as a strategy to intelligently select the expert(s) with the highest estimated labeling accuracy. IEThresh estimates a confidence interval for the reliability of each expert and filters out the one(s) whose estimated upper-bound c...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Pinar Donmez, Jaime G. Carbonell, Jeff Schneider
• Keywords: Data Mining, Interval Estimate Threshold, KDD 2009, Multiple Labeling Sources, Total Labeling Effort