- author = "Martijn Bot and William B. Langdon",
- title = "Application of Genetic Programming to Induction of Linear Classification Trees",
- booktitle = "Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99)",
- year = "1999",
- editor = "Eric Postma and Marc Gyssens",
- pages = "107--114",
- address = "Kasteel Vaeshartelt, Maastricht, Holland",
- month = "3-4 " # nov,
- organisation = "BNVKI, Dutch and the Belgian AI Association",
- keywords = "genetic algorithms, genetic programming, data mining",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/BNAIC99.bot.18aug99.ps.gz",
- size = "8 pages",
- abstract = "A common problem in datamining is to find accurate classifiers for a dataset. For this purpose, genetic programming (GP) is applied to a benchmark of classification problems. In particular, using GP we are able to induce decision trees with a linear combination of variables in each function node. The effects of techniques as limited error fitness, fitness sharing Pareto scoring and domination Pareto scoring are evaluated. Results indicate that GP can be applied successfully to classification problems. Comparisons with current state-of-the-art algorithms in machine learning are presented and areas of future research are identified.",
-
notes = "http://www.bnvki.org/
https://documentserver.uhasselt.be/handle/1942/4193
July 2024 broken http://www.cs.unimaas.nl/~bnvki/",
