Multi-objective genetic programming optimization of decision trees for classifying medical data

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 {tshape a priori}. It is possible for such decisions to be made {tshape 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.</p

Multi-objective genetic programming optimization of decision trees for classifying medical data

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 {tshape a priori}. It is possible for such decisions to be made {tshape 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