ACM Computing Surveys (CSUR)
Discernibility Matrix Approach to Exception Analysis
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Discovery of M-of-N Concepts for Classification
DS '00 Proceedings of the Third International Conference on Discovery Science
Multilevel data summarization from information systems: a "rule + exception" approach
AI Communications - Special issue on Artificial intelligence advances in China
Multilevel data summarization from information systems: a “rule + exception” approach
AI Communications - Artificial Intelligence Advances in China
| Hi-index | 0.00 |
This paper presents a method for learning graded concepts. Our method uses a hybrid concept representation that integrates numeric weights and thresholds with rules and combines rules with exemplars. Concepts are learned by constructing general descriptions to represent common cases. These general descriptions are in the form of decision rules with weights on conditions, interpreted by a similarity measure and numeric thresholds. The exceptional cases are represented as exemplars. This method was implemented in the Flexible Concept Learning System (FCLS) and tested on a variety of problems. The testing problems included practical concepts, concepts with graded structures, and concepts that can be defined in the classic view. For comparison, a decision tree learning system, an instance-based learning system, and the basic rule learning variant of FCLS were tested on the same problems. The results have shown a statistically meaningful advantage of the proposed method over others both in terms of classification accuracy and description simplicity on several problems.