Rule analysis with rough sets theory

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. © 2006 IEEE.