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      Modeling interestingness of streaming classification rules as a classification problem

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      Author
      Aydın, Tolga
      Güvenir, Halil Altay
      Date
      2005-06
      Source Title
      Turkish Symposium on Artificial Intelligence and Neural Networks TAINN, 2005
      Publisher
      Springer
      Pages
      168 - 176
      Language
      English
      Type
      Conference Paper
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      Abstract
      Inducing classification rules on domains from which information is gathered at regular periods lead the number of such classification rules to be generally so huge that selection of interesting ones among all discovered rules becomes an important task. At each period, using the newly gathered information from the domain, the new classification rules are induced. Therefore, these rules stream through time and are so called streaming classification rules. In this paper, an interactive classification rules' interestingness learning algorithm (ICRIL) is developed to automatically label the classification rules either as "interesting" or "uninteresting" with limited user interaction. In our study, VFFP (Voting Fuzzified Feature Projections), a feature projection based incremental classification algorithm, is also developed in the framework of ICRIL. The concept description learned by the VFFP is the interestingness concept of streaming classification rules. © Springer-Verlag Berlin Heidelberg 2006.
      Keywords
      Classification (of information)
      Data processing
      Learning algorithms
      Logic programming
      User interfaces
      Incremental classification algorithms
      Interestingness learning algorithm (ICRIL)
      Streaming classification
      Problem solving
      Permalink
      http://hdl.handle.net/11693/27255
      Published Version (Please cite this version)
      http://dx.doi.org/10.1007/11803089_20
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      • Department of Computer Engineering 1418
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