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<item>
  <id>06046563</id>
  <dt>a</dt>
  <an>06046563</an>
  <augroup>
    <au>I\v{s}a, Ji\v{r}{\'\i}</au>
    <au>Reitermanov\'a, Zuzana</au>
    <au>S\'ykora, Ond\v{r}ej</au>
  </augroup>
  <ti>Cost-sensitive classification with unconstrained influence diagrams.</ti>
  <so>Bielikov\'a, M\'aria (ed.) et al., SOFSEM 2012: Theory and practice of computer science. 38th conference on current trends in theory and practice of computer science, \v{S}pindler{\uu}v Ml\'yn, Czech Republic, January 21--27, 2012. Proceedings. Berlin: Springer (ISBN 978-3-642-27659-0/pbk). Lecture Notes in Computer Science 7147, 625-636 (2012).</so>
  <py>2012</py>
  <pu>Berlin: Springer</pu>
  <lagroup>
    <la>EN</la>
  </lagroup>
  <ccgroup>
  </ccgroup>
  <utgroup>
  </utgroup>
  <cigroup>
  </cigroup>
  <ligroup>
    <li>doi:10.1007/978-3-642-27660-6_51</li>
  </ligroup>
  <abgroup>
    <ab>Summary: In this paper, we deal with an enhanced problem of cost-sensitive classification, where not only the cost of misclassification needs to be minimized, but also the total cost of tests and their requirements. To solve this problem, we propose a novel method CS-UID based on the theory of Unconstrained Influence Diagrams (UIDs). We empirically evaluate and compare CS-UID with an existing algorithm for test-cost sensitive classification (TCSNB) on multiple real-world public referential datasets. We show that CS-UID outperforms TCSNB.</ab>
    <rv></rv>
  </abgroup>
</item>