Paper Info
Title
Multi-objective Genetic Programming Optimization of Decision Trees for Classifying Medical Data
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 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.
Year
DOI
Venue
2003
10.1007/978-3-540-45224-9_42
LECTURE NOTES IN COMPUTER SCIENCE
Keywords
Field
DocType
decision tree
Decision tree,Computer science,A priori and a posteriori,Decision tree model,Multi-objective optimization,Genetic programming,Artificial intelligence,Decision tree learning,Genetic program,Machine learning,Incremental decision tree
Conference
Volume
ISSN
Citations 
2773
0302-9743
13
PageRank 
References 
Authors
0.59
11
2
Name
Order
Citations
PageRank
Ernest Muthomi Mugambi1130.59
Andrew Hunter217511.31