Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic
  Decision Tree Induction Algorithm
  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.
