Genetic programming for knowledge discovery in chest-pain diagnosis
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{bojarczuk:2000:kdcp,
-
author = "Celia C. Bojarczuk and Heitor S. Lopes and
Alex A. Freitas",
-
title = "Genetic programming for knowledge discovery in
chest-pain diagnosis",
-
journal = "IEEE Engineering in Medicine and Biology Magazine",
-
year = "2000",
-
volume = "19",
-
number = "4",
-
pages = "38--44",
-
month = jul # "-" # aug,
-
keywords = "genetic algorithms, genetic programming, data mining,
knowledge discovery, chest-pain diagnosis, predictive
accuracy, rule set, comprehensible rules, background
knowledge, preprocessing step, data sets, medical
applications",
-
ISSN = "0739-5175",
-
URL = "http://ieeexplore.ieee.org/iel5/51/18543/00853480.pdf",
-
URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pubs.html",
-
URL = "http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/IEEE-EMB-2000.ps",
-
URL = "http://citeseer.ist.psu.edu/459907.html",
-
size = "7 pages",
-
abstract = "Explores a promising data mining approach. Despite the
small number of examples available in the authors'
application domain (taking into account the large
number of attributes), the results of their experiments
can be considered very promising. The discovered rules
had good performance concerning predictive accuracy,
considering both the rule set as a whole and each
individual rule. Furthermore, what is more important
from a data mining viewpoint, the system discovered
some comprehensible rules. It is interesting to note
that the system achieved very consistent results by
working from {"}tabula rasa,{"} without any background
knowledge, and with a small number of examples. The
authors emphasize that their system is still in an
experiment in the research stage of development.
Therefore, the results presented here should not be
used alone for real-world diagnoses without consulting
a physician. Future research includes a careful
selection of attributes in a preprocessing step, so as
to reduce the number of attributes (and the
corresponding search space) given to the GP. Attribute
selection is a very active research area in data
mining. Given the results obtained so far, GP has been
demonstrated to be a really useful data mining tool,
but future work should also include the application of
the GP system proposed here to other data sets, to
further validate the results reported in this
article.",
-
notes = "lilgp",
- }
Genetic Programming entries for
Celia Cristina Bojarczuk
Heitor Silverio Lopes
Alex Alves Freitas
Citations