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From: jair-ed@ptolemy.arc.nasa.gov
Subject: New Article, Cost-Sensitive Classification
Message-ID: <1995Mar29.002715.4936@ptolemy-ethernet.arc.nasa.gov>
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Date: Wed, 29 Mar 1995 00:27:15 GMT
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JAIR is pleased to announce the publication of the following article:

Turney, P.D. (1995)
  "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid 
   Genetic Decision Tree Induction Algorithm", Volume 2, pages 369-409.
   PostScript: volume2/turney95a.ps (474K)
               compressed, volume2/turney95a.ps.Z (183K)

   Abstract: This paper introduces ICET, a new algorithm for
   cost-sensitive classification. ICET uses a genetic algorithm to evolve
   a population of biases for a decision tree induction algorithm. The
   fitness function of the genetic algorithm is the average cost of
   classification when using the decision tree, including both the costs
   of tests (features, measurements) and the costs of classification
   errors. ICET is compared here with three other algorithms for
   cost-sensitive classification - EG2, CS-ID3, and IDX - and also with
   C4.5, which classifies without regard to cost. The five algorithms are
   evaluated empirically on five real-world medical datasets. Three sets
   of experiments are performed.  The first set examines the baseline
   performance of the five algorithms on the five datasets and
   establishes that ICET performs significantly better than its
   competitors. The second set tests the robustness of ICET under a
   variety of conditions and shows that ICET maintains its advantage. The
   third set looks at ICET's search in bias space and discovers a way to
   improve the search.


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