Please use this identifier to cite or link to this item: https://ir.sc.mahidol.ac.th/handle/123456789/110
Title: Rule analysis with rough sets theory
Authors: Puntip Pattaraintakorn
Cercone, Nick
Kanlaya Naruedomkul
Keywords: Rough sets;Rule learning;Rule reducts;Decision rule;Postprocessing;RuleIntelligent system
Issue Date: 2006
Publisher: IEEE
Citation: Pattaraintakorn, P. "Analysis of distributed databases with a hybrid rough sets approach", Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on, On page(s): 2158 - 2163
Abstract: Postprocessing is a significant step in the data analysis process which is often ignored or glossed over. Once we have a large set of generated rules, how can we elicit the sufficient and necessary rules? In this paper, we propose an alternative approach for decision rule learning with rough sets theory in the postprocessing step called ‘ROSERULE’. Essentially, we introduce rule reducts, a sufficient and necessary part which preserves classification of the rule universe, as a rough sets tool for rule analysis. ROSERULE learns and analyzes from the rule set to generate rule reducts which can be used to reduce the number of the rules. This is in contrast to common rule analysis which simply performs rule selection. We illustrate the performance of ROSERULE with several case studies; melanoma, primary biliary cirrhosis, pneumonia and a real-world case study, geriatric data sets. ROSERULE is run on these data sets and the result are a reduced number of rules that successfully preserve the original classification.
Description: Published in Granular Computing, 2006 IEEE International Conference on
URI: http://ir.sc.mahidol.ac.th/handle/123456789/110
ISBN: 1-4244-0134-8
Appears in Collections:Mathematics: International Proceedings

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.