Although there has been considerable study in the area of trading- off accuracy and comprehensibility of decision tree models, the bulk of the methods dwell on sacrificing comprehensibility for the sake of accuracy, or fine-tuning the balance between comprehensibility and accuracy. Invariably, the level of trade-off is decided a priori. It is possible for such decisions to be made a posteriori which means the induction process does not discriminate against any of the objectives. In this paper, we present such a method that uses multi-objective Genetic Programming to optimize decision tree models. We have used this method to build decision tree models from Diabetes data in a bid to investigate its capability to trade-off comprehensibility and performance.
CITATION STYLE
Mugambi, E. M., & Hunter, A. (2003). Multi-objective genetic programming optimization of decision trees for classifying medical data. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2773 PART 1, pp. 293–299). Springer Verlag. https://doi.org/10.1007/978-3-540-45224-9_42
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