On using rule induction in multiple classifiers with a combiner aggregation strategy
(2005) 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1002757
- author
- Stefanowski, Jerzy and Nowaczyk, Slawomir LU
- organization
- publishing date
- 2005
- 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
- host publication
- 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)
- conference location
- Wroclaw, Poland
- conference dates
- 2005-09-08 - 2005-09-10
- 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
- 2016-04-04 14:09:29
- date last changed
- 2022-01-30 01:34:08
@inproceedings{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}}, booktitle = {{Proceedings of the Fifth International Conference on Intelligent Systems Design and Applications (ISDA 2005)}}, isbn = {{0-7695-2286-6}}, keywords = {{knowledge based systems; knowledge representation; learning (artificial intelligence); pattern classification; rough set theory}}, language = {{eng}}, pages = {{432--437}}, title = {{On using rule induction in multiple classifiers with a combiner aggregation strategy}}, url = {{http://dx.doi.org/10.1109/ISDA.2005.74}}, doi = {{10.1109/ISDA.2005.74}}, year = {{2005}}, }