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      • 2000 Working Papers
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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 2000 Working Papers
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      Interactive machine learning–letting users build classifiers

      Ware, Malcolm; Frank, Eibe; Holmes, Geoffrey; Hall, Mark A.; Witten, Ian H.
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      Ware, M., Frank, E., Holmes, G., Hall, M. & Witten, I.H. (2000). Interactive machine learning–letting users build classifiers. (Working paper 00/04). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
      Permanent Research Commons link: https://hdl.handle.net/10289/1020
      Abstract
      According to standard procedure, building a classifier is a fully automated process that follows data preparation by a domain expert. In contrast, interactive machine learning engages users in actually generating the classifier themselves. This offers a natural way of integrating background knowledge into the modeling stage–so long as interactive tools can be designed that support efficient and effective communication. This paper shows that appropriate techniques can empower users to create models that compete with classifiers built by state-of-the-art learning algorithms. It demonstrates that users–even users who are not domain experts–can often construct good classifiers, without any help from a learning algorithm, using a simple two-dimensional visual interface. Experiments demonstrate that, not surprisingly, success hinges on the domain: if a few attributes can support good predictions, users generate accurate classifiers, whereas domains with many high-order attribute interactions favor standard machine learning techniques. The future challenge is to achieve a symbiosis between human user and machine learning algorithm.
      Date
      2000-03
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      00/04
      Publisher
      University of Waikato, Department of Computer Science
      Collections
      • 2000 Working Papers [12]
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