Authors
Sung-Hyuk Cha, Charles C Tappert
Publication date
2009/2/25
Journal
Journal of pattern recognition research
Volume
4
Issue
1
Pages
1-13
Description
Tree-based classifiers are important in pattern recognition and have been well studied. Although the problem of finding an optimal decision tree has received attention, it is a hard optimization problem. Here we propose utilizing a genetic algorithm to improve on the finding of compact, near-optimal decision trees. We present a method to encode and decode a decision tree to and from a chromosome where genetic operators such as mutation and crossover can be applied. Theoretical properties of decision trees, encoded chromosomes, and fitness functions are presented.
Total citations
Scholar articles
SH Cha, CC Tappert - Journal of pattern recognition research, 2009