Finding the tree in the Forest
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{Konig:2010:IADIS,
-
author = "Rikard Konig and Ulf Johansson and Lars Niklasson",
-
title = "Finding the tree in the Forest",
-
booktitle = "Proceedings of the IADIS International Conference
Applied Computing",
-
year = "2010",
-
editor = "Hans Weghorn and Pedro Isaias and Radu Vasiu",
-
pages = "135--142",
-
address = "Timisoara, Romania",
-
month = "14-16 " # oct,
-
organisation = "IADIS",
-
keywords = "genetic algorithms, genetic programming, decision
support, decision trees, alternative solutions,
inconsistency",
-
isbn13 = "978-972-8939-30-4",
-
URL = "http://www.iadisportal.org/applied-computing-2010-proceedings",
-
URL = "http://bada.hb.se/bitstream/2320/6870/1/Finding%20the%20Tree%20In%20The%20forest.pdf",
-
size = "8 pages",
-
abstract = "Decision trees are often used for decision support
since they are fast to train, easy to understand and
deterministic; i.e., always create identical trees from
the same training data. This property is, however, only
inherent in the actual decision tree algorithm,
nondeterministic techniques such as genetic programming
could very well produce different trees with similar
accuracy and complexity for each execution. Clearly, if
more than one solution exists, it would be misleading
to present a single tree to a decision maker. On the
other hand, too many alternatives could not be handled
manually, and would only lead to confusion. Hence, we
argue for a method aimed at generating a suitable
number of alternative decision trees with comparable
accuracy and complexity. When too many alternative
trees exist, they are grouped and representative
accurate solutions are selected from each group. Using
domain knowledge, a decision maker could then select a
single best tree and, if required, be presented with a
small set of similar solutions, in order to further
improve his decisions. In this paper, a method for
generating alternative decision trees is suggested and
evaluated. All in all,four different techniques for
selecting accurate representative trees from groups of
similar solutions are presented. Experiments on 19 UCI
data sets show that it often exist dozens of
alternative trees, and that one of the evaluated
techniques clearly outperforms all others for selecting
accurate and representative models.",
- }
Genetic Programming entries for
Rikard Konig
Ulf Johansson
Lars Niklasson
Citations