Improving GP Classification Performance by Injection of Decision Trees
2010 (English)Conference paper (Refereed)
This paper presents a novel hybrid method
combining genetic programming and decision tree learning.
The method starts by estimating a benchmark level of
reasonable accuracy, based on decision tree performance on
bootstrap samples of the training set. Next, a normal GP
evolution is started with the aim of producing an accurate GP.
At even intervals, the best GP in the population is evaluated
against the accuracy benchmark. If the GP has higher accuracy
than the benchmark, the evolution continues normally until the
maximum number of generations is reached. If the accuracy is
lower than the benchmark, two things happen. First, the fitness
function is modified to allow larger GPs, able to represent more
complex models. Secondly, a decision tree with increased size
and trained on a bootstrap of the training data is injected into
the population. The experiments show that the hybrid solution
of injecting decision trees into a GP population gives synergetic
effects producing results that are better than using either
technique separately. The results, from 18 UCI data sets, show
that the proposed method clearly outperforms normal GP, and
is significantly better than the standard decision tree algorithm.
Place, publisher, year, edition, pages
IEEE , 2010.
genetic programming, tree induction, Machine Learning
Computer Science Computer and Information Science
IdentifiersURN: urn:nbn:se:hb:diva-6415DOI: 10.1109/CEC.2010.5585988Local ID: 2320/6868ISBN: 978-1-4244-6909-3OAI: oai:DiVA.org:hb-6415DiVA: diva2:887103
WCCI 2010 IEEE World Congress on Computational Intelligence, CEC 2010