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<item>
  <id>01767635</id>
  <dt>j</dt>
  <an>01767635</an>
  <augroup>
    <au>Podgorelec, Vili</au>
    <au>Kokol, Peter</au>
  </augroup>
  <ti>Evolutionary induced decision trees for dangerous software modules prediction.</ti>
  <so>Inf. Process. Lett. 82, No.1, 31-38 (2002).</so>
  <py>2002</py>
  <pu>Elsevier Sciences Publishers (North-Holland), Amsterdam</pu>
  <lagroup>
    <la>EN</la>
  </lagroup>
  <ccgroup>
    <cc>D.0</cc>
    <cc>I.2.6</cc>
    <cc>G.4</cc>
  </ccgroup>
  <utgroup>
    <ut>software engineering</ut>
    <ut>decision making</ut>
    <ut>decision trees</ut>
    <ut>evolutionary algorithms</ut>
    <ut>software fault prediction</ut>
  </utgroup>
  <cigroup>
  </cigroup>
  <ligroup>
    <li>doi:10.1016/S0020-0190(01)00284-8</li>
  </ligroup>
  <abgroup>
    <ab>We study the possibility of constructing decision trees with evolutionary algorithms in order to increase their predictive accuracy. We present a self-adapting evolutionary algorithm for the induction of decision trees and describe the principle of decision making based on multiple evolutionary induced decision trees -- decision forest. The developed model is used as a fault predictive approach to foresee dangerous software modules, which identification can largely enhance the reliability of software.</ab>
    <rv></rv>
  </abgroup>
</item>