Return on Investment for Active Learning

نویسندگان

  • Robbie A. Haertel
  • Kevin D. Seppi
  • Eric K. Ringger
  • James L. Carroll
چکیده

Active Learning (AL) can be defined as a selectively supervised learning protocol intended to present those data to an oracle for labeling which will be most enlightening for machine learning. While AL traditionally accounts for the value of the information obtained, it often ignores the cost of obtaining the information thus causing it to perform sub-optimally with respect to total cost. We present a framework for AL that accounts for this cost and discuss optimality and tractability in this framework. Using this framework we motivate Return On Investment (ROI), a practical, cost-sensitive heuristic that can be used to convert existing algorithms into cost-conscious active learners. We demonstrate the validity of ROI in a simulated AL part-of-speech tagging task on the Penn Treebank in which ROI achieves as high as a 73% reduction in hourly cost over random selection.

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تاریخ انتشار 2008