Abstract
At present regression trees tend to be accurate, however they can be incomprehensible to experts. The proposed algorithm Economic Generalized Regression (EGR) induces regression trees that are more logical and convenient. EGR uses domain knowledge. The domain knowledge contains “is-a” hierarchies and cost associated to each variable. After generating several subtrees from training examples, EGR selects the best one according to a user-defined balance between accuracy and average classification cost. The user can define the degree of economy and generalization. This information will influence directly on the quality of search that the algorithm must undertake.
This work has been partially supported by project FACA number PB98–0937–C04–01 of the CICYT, Spain. FACA is apart of the FRESCO project.
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Núñez, M. (2000). Generalized regression trees1 . In: Bethlehem, J.G., van der Heijden, P.G.M. (eds) COMPSTAT. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57678-2_48
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DOI: https://doi.org/10.1007/978-3-642-57678-2_48
Publisher Name: Physica, Heidelberg
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