International Journal of Man-Machine Studies
Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Creating Ensembles of Classifiers
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Learning classifiers from distributed, semantically heterogeneous, autonomous data sources
Learning classifiers from distributed, semantically heterogeneous, autonomous data sources
A theory of learning with similarity functions
Machine Learning
Feature Selection Algorithm for Multiple Classifier Systems: A Hybrid Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Similarity-based Classification: Concepts and Algorithms
The Journal of Machine Learning Research
An inquiry into anatomy of conflicts
Information Sciences: an International Journal
Local and global approximations for incomplete data
Transactions on rough sets VIII
Multi-Agent Decision Taking System
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Application of the Method of Editing and Condensing in the Process of Global Decision-making
Fundamenta Informaticae
Multimodal classification: case studies
Transactions on Rough Sets V
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
A Rough Set Approach to Multiple Classifier Systems
Fundamenta Informaticae - SPECIAL ISSUE ON CONCURRENCY SPECIFICATION AND PROGRAMMING (CS&P 2005) Ruciane-Nide, Poland, 28-30 September 2005
Music Recommendation Based on Multidimensional Description and Similarity Measures
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
Complex Decision Systems and Conflicts Analysis Problem
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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The paper includes a discussion of issues related to the process of global decision-making on the basis of information stored in several local knowledge bases. The local knowledge bases contain information on the same subject, but are defined on different sets of conditional attributes that are not necessarily disjoint. A decision-making system, which uses a number of knowledge bases, makes global decisions on the basis of a set of conditional attributes specified for all of the local knowledge bases used. The paper contains a description of a multi-agent decision-making system with a hierarchical structure. Additionally, it briefly overviews methods of inference that enable global decision-making in this system and that were proposed in our earlier works. The paper also describes the application of the conditional attributes reduction technique to local knowledge bases. Our main aim was to investigate the effect of attribute reduction on the efficiency of inference in such a system. For a measure of the efficiency of inference, we mean mainly an error rate of classification, for which a definition is given later in this paper. Therefore, our goal was to reduce the error rate of classification.