Computers & Mathematics with Applications
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Rule evaluations, attributes, and rough sets: extension and a case study
Transactions on rough sets VI
Hybrid rough sets-population based system
Transactions on rough sets VII
Hybrid rough sets intelligent system architecture for survival analysis
Transactions on rough sets VII
Hybrid ensemble approach for classification
Applied Intelligence
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
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It is difficult to provide significant insight into any hybrid intelligent system design. We offer an informative account of the basic ideas underlying hybrid intelligent systems. We propose a balanced approach to constructing a hybrid intelligent system for a medical domain, along with arguments in favor of this balance and mechanisms for achieving a proper balance. This first of a series of contributions to hybrid intelligent systems design focuses on selecting attributes for soft-computing analysis. One part of this first contribution in our system is developed. Two definitions, probe and probe reducts, are introduced. Our CDispro algorithm can produce the core attribute and reducts that are essential condition attributes in data sets. Our initial study tests data from the UCI repository and geriatric data from DalMedix. The performance and utility of generated reducts are evaluated by 3-fold cross-validation that illustrates reduced dimensionality and complexity of data sets and processes.