Case-based reasoning
Artificial Intelligence: A Guide to Intelligent Systems
Artificial Intelligence: A Guide to Intelligent Systems
Learning premises of fuzzy rules for knowledge acquisition in classification problems
Knowledge and Information Systems
Learning Classification Rules Using Lattices (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Case-Based Classification Using Similarity-Based Retrieval
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
A Hybrid Case Based Reasoning System Using Fuzzy-Rough Sets and Formal Concept Analysis
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Compact fuzzy association rule-based classifier
Expert Systems with Applications: An International Journal
Formal concept analysis in information science
Annual Review of Information Science and Technology
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Classification rule acquisition based on extended concept lattice
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Formal concept analysis as mathematical theory of concepts and concept hierarchies
Formal Concept Analysis
Incremental classification rules based on association rules using formal concept analysis
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
A new similarity measure in formal concept analysis for case-based reasoning
Expert Systems with Applications: An International Journal
A new case-based classification using incremental concept lattice knowledge
Data & Knowledge Engineering
Hi-index | 0.01 |
The focus of this paper is a construction of better knowledge base in case-based classifier system. Our knowledge base structure is based on concept lattice where rules are built from its subconcept-superconcept relation. Since the lattice can only be constructed from inputs with binary attributes, descriptive and numeric attributes must be transformed to binary attributes. In this paper, we propose the transformation of numeric attributes to descriptive attributes using fuzzy set theory. We experiment on benchmark data sets, Car and Iris, to determine the performance in term of number of rules used and classification precision. The results show that trend of accuracy is proportional to the size of learning inputs. The number of rules used is relatively small compared with size of training data. Our case-based classifier produces very promising results in practice and can classify the new problem more accurate than traditional classifiers.