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CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

  • Dustin ArendtEmail author
  • Caner Komurlu
  • Leslie M. Blaha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10284)

Abstract

We developed CHISSL, a human-machine interface that utilizes interactive supervision to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user’s interactions, CHISSL trains a classification model guided by the user’s grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human and machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.

Keywords

Human-machine interface Interactive clustering Active learning Semi-supervised learning Direct manipulation 

Notes

Acknowledgments

The research described in this document was sponsored the U.S. Department of Energy (DOE) through the Analysis in Motion Initiative at Pacific Northwest National Laboratory. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Dustin Arendt
    • 1
    Email author
  • Caner Komurlu
    • 2
  • Leslie M. Blaha
    • 1
  1. 1.Pacific Northwest National LaboratoryRichlandUSA
  2. 2.Illinois Institute of TechnologyChicagoUSA

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