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On using rule induction in multiple classifiers with a combiner aggregation strategy

Stefanowski, Jerzy and Nowaczyk, Slawomir LU (2005) Fifth International Conference on Intelligent Systems Design and Applications (ISDA 2005) In Proceedings of the Fifth International Conference on Intelligent Systems Design and Applications (ISDA 2005) p.432-437
Abstract
The paper is an experimental study of using the rough sets based rule induction algorithm MODLEM in the framework of multiple classifiers. Particular attention is paid to using a meta-classifier called combiner, which learns how to aggregate answers of component classifiers. The experimental results confirm that the range of classification improvement for the combiner depends on the independence of errors made by the component classifiers. Moreover, we summarize the experience with using MODLEM in other multiple classifiers, namely the bagging and n/sup 2/ classifiers.
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
knowledge based systems, knowledge representation, learning (artificial intelligence), pattern classification, rough set theory
in
Proceedings of the Fifth International Conference on Intelligent Systems Design and Applications (ISDA 2005)
pages
432 - 437
conference name
Fifth International Conference on Intelligent Systems Design and Applications (ISDA 2005)
external identifiers
  • Scopus:33847004781
ISBN
0-7695-2286-6
DOI
10.1109/ISDA.2005.74
language
English
LU publication?
yes
id
892e8440-9bdf-4375-833e-b92c5b6cce7a (old id 1002757)
date added to LUP
2008-02-01 13:36:16
date last changed
2016-10-13 05:00:18
@misc{892e8440-9bdf-4375-833e-b92c5b6cce7a,
  abstract     = {The paper is an experimental study of using the rough sets based rule induction algorithm MODLEM in the framework of multiple classifiers. Particular attention is paid to using a meta-classifier called combiner, which learns how to aggregate answers of component classifiers. The experimental results confirm that the range of classification improvement for the combiner depends on the independence of errors made by the component classifiers. Moreover, we summarize the experience with using MODLEM in other multiple classifiers, namely the bagging and n/sup 2/ classifiers.},
  author       = {Stefanowski, Jerzy and Nowaczyk, Slawomir},
  isbn         = {0-7695-2286-6},
  keyword      = {knowledge based systems,knowledge representation,learning (artificial intelligence),pattern classification,rough set theory},
  language     = {eng},
  pages        = {432--437},
  series       = {Proceedings of the Fifth International Conference on Intelligent Systems Design and Applications (ISDA 2005)},
  title        = {On using rule induction in multiple classifiers with a combiner aggregation strategy},
  url          = {http://dx.doi.org/10.1109/ISDA.2005.74},
  year         = {2005},
}