This wrapper approach is based on the Nondominated Sorting Genetic Algorithm II (NSGA-II) with an enclosed classification tree algorithm. It performs cost-sensitive classification by solving the two-objective optimization problem of minimizing misclassification rate and minimizing total costs for classification.
Simulation of data with one grouping variable containing four classes and 20 explanatory variables. Variables X1 to X3 are informative for seperating the four classes. Variable X1 separates class 1, X2 separates class 1 and class 2, and X3 separates class 3 from class 4. Variables X4, X5, and X6 are created on basis of X3 and can also be used to separate class 3 from class 4 but with decreased prediction accuracy.
● Data Source:
CranContrib
● Keywords: Non-hierarchical evolutionary multi-objective tree learner
● Alias: Sim_Data
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NHEMOtree performs cost-sensitive classification by solving the two-objective optimization problem of minimizing misclassification rate and minimizing total costs for classification. The three methods comprised in NHEMOtree ("NHEMO", "NHEMO_Cutoff", "Wrapper") are based on EMOAs with either tree representation or bitstring representation with a classification tree as enclosed classifier.
NHEMO_Cutoff performs cost-sensitive classification by solving the non-hierarchical evolutionary two-objective optimization problem of minimizing misclassification rate and minimizing total costs for classification. NHEMO_Cutoff is based on an EMOA with tree representation and local cutoff optimization. Cutoffs of the tree learner are optimized analogous to a classification tree with recursive partitioning either based on the Gini index or the misclassification rate.
NHEMO performs cost-sensitive classification by solving the non-hierarchical evolutionary two-objective optimization problem of minimizing misclassification rate and minimizing total costs for classification. NHEMO is based on an EMOA with tree representation and cutoff optimization conducted by the EMOA.
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