Turney, P.D. (1995)
"Cost-Sensitive Classification: Empirical Evaluation of a Hybrid
Genetic Decision Tree Induction Algorithm", Volume 2, pages 369-409.
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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