Multi-objective Genetic Programming Optimization of Decision Trees for Classifying Medical Data

  • Ernest Muthomi Mugambi
  • Andrew Hunter
Conference paper

DOI: 10.1007/978-3-540-45224-9_42

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2773)
Cite this paper as:
Mugambi E.M., Hunter A. (2003) Multi-objective Genetic Programming Optimization of Decision Trees for Classifying Medical Data. In: Palade V., Howlett R.J., Jain L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science, vol 2773. Springer, Berlin, Heidelberg

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Ernest Muthomi Mugambi
    • 1
  • Andrew Hunter
    • 2
  1. 1.Science Lab, Computer Science Dept.Sunderland UniversityDurhamUK
  2. 2.Durham UniversityUK

Personalised recommendations