Rough set algorithms in classification problem
Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
DBROUGH: A Rough Set Based Knowledge Discovery System
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Rough Set Based Data Exploration Using ROSE System
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Finding Reducts in Composed Information Systems
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
Rough Set Approach to the Survival Analysis
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
ELEM2: A Learning System for More Accurate Classifications
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
CBMS '01 Proceedings of the Fourteenth IEEE Symposium on Computer-Based Medical Systems
A new rough sets model based on database systems
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
Hybrid Intelligent Systems: Selecting Attributes for Soft-Computing Analysis
COMPSAC '05 Proceedings of the 29th Annual International Computer Software and Applications Conference - Volume 01
Selecting attributes for soft-computing analysis in hybrid intelligent systems
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Introducing a rule importance measure
Transactions on Rough Sets V
| Hi-index | 0.00 |
Manually evaluating important and interesting rules generated from data is generally infeasible due to the large number of rules extracted. Different approaches such as rule interestingness measures and rule quality measures have been proposed and explored previously to extract interesting and high quality association rules and classification rules. Rough sets theory was originally presented as an approach to approximate concepts under uncertainty. In this paper, we explore rough sets based rule evaluation approaches in knowledge discovery. We demonstrate rule evaluation approaches through a real-world geriatric care data set from Dalhousie Medical School. Rough set based rule evaluation approaches can be used in a straightforward way to rank the importance of the rules. One interesting system developed along these lies in HYRIS (HYbrid Rough sets Intelligent System). We introduce HYRIS through a case study on survival analysis using the geriatric care data set.