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

Mugambi, Ernest Muthomi and Hunter, Andrew (2003) Multi-objective genetic programming optimization of decision trees for classifying medical data. In: Seventh International Conference on Knowledged-Based Intelligent Information & Engineering Systems, 3-5 September 2003, University of Oxford, UK.

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Full text URL: http://dx.doi.org/10.1007/978-3-540-45224-9_42

Abstract

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.

Item Type:Conference or Workshop Item (Paper)
Additional Information: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.
Keywords:decision trees, multi-objective genetic programming, classifying medical data, optimize decision tree
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:2827
Deposited By: Rosaline Smith
Deposited On:09 Jul 2010 13:36
Last Modified:30 Apr 2013 08:54

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